


from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]=2				,							SCREAMING_SNAKE_CASE      :							List[str]=3				,							SCREAMING_SNAKE_CASE      :							Tuple=4				,							SCREAMING_SNAKE_CASE      :							Any=2				,							SCREAMING_SNAKE_CASE      :							Any=7				,							SCREAMING_SNAKE_CASE      :							str=True				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							SCREAMING_SNAKE_CASE      :							Optional[int]=True				,							SCREAMING_SNAKE_CASE      :							Dict=True				,							SCREAMING_SNAKE_CASE      :							Any=99				,							SCREAMING_SNAKE_CASE      :							Dict=36				,							SCREAMING_SNAKE_CASE      :							Tuple=2				,							SCREAMING_SNAKE_CASE      :							List[Any]=4				,							SCREAMING_SNAKE_CASE      :							List[str]=37				,							SCREAMING_SNAKE_CASE      :							Dict="gelu"				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=0.1				,							SCREAMING_SNAKE_CASE      :							str=0.1				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=512				,							SCREAMING_SNAKE_CASE      :							str=16				,							SCREAMING_SNAKE_CASE      :							List[Any]=2				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.02				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=6				,							SCREAMING_SNAKE_CASE      :							Optional[int]=6				,							SCREAMING_SNAKE_CASE      :							str=3				,							SCREAMING_SNAKE_CASE      :							str=4				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE      :							str=1000				,							):
           _A					:     Any     = parent
           _A					:     Tuple     = batch_size
           _A					:     List[Any]     = num_channels
           _A					:     Optional[int]     = image_size
           _A					:     List[Any]     = patch_size
           _A					:     Dict     = is_training
           _A					:     List[str]     = use_input_mask
           _A					:     Tuple     = use_token_type_ids
           _A					:     Dict     = use_labels
           _A					:     Dict     = vocab_size
           _A					:     int     = hidden_size
           _A					:     str     = num_hidden_layers
           _A					:     str     = num_attention_heads
           _A					:     int     = intermediate_size
           _A					:     Union[str, Any]     = hidden_act
           _A					:     str     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     Optional[Any]     = max_position_embeddings
           _A					:     List[Any]     = type_vocab_size
           _A					:     List[str]     = type_sequence_label_size
           _A					:     Union[str, Any]     = initializer_range
           _A					:     str     = coordinate_size
           _A					:     Any     = shape_size
           _A					:     Union[str, Any]     = num_labels
           _A					:     List[Any]     = num_choices
           _A					:     Union[str, Any]     = scope
           _A					:     List[str]     = range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           _A					:     List[str]     = text_seq_length
           _A					:     List[str]     = (image_size // patch_size) ** 2 + 1
           _A					:     Any     = self.text_seq_length + self.image_seq_length


    def        A   (  self      :							Dict):
           _A					:     Optional[int]     = ids_tensor([self.batch_size, self.text_seq_length]				,							self.vocab_size)

           _A					:     str     = ids_tensor([self.batch_size, self.text_seq_length, 4]				,							self.range_bbox)
           _A					:     Tuple     = bbox.numpy()
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]):
                  for j in range(bbox.shape[1]):
                         if bbox[i, j, 3] < bbox[i, j, 1]:
                                _A					:     List[Any]     = bbox[i, j, 3]
                                _A					:     Optional[Any]     = bbox[i, j, 1]
                                _A					:     int     = tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                _A					:     str     = bbox[i, j, 2]
                                _A					:     Tuple     = bbox[i, j, 0]
                                _A					:     List[str]     = tmp_coordinate
           _A					:     List[Any]     = tf.constant(SCREAMING_SNAKE_CASE)

           _A					:     Dict     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A					:     str     = None
           if self.use_input_mask:
                  _A					:     Dict     = random_attention_mask([self.batch_size, self.text_seq_length])

           _A					:     Tuple     = None
           if self.use_token_type_ids:
                  _A					:     Optional[int]     = ids_tensor([self.batch_size, self.text_seq_length]				,							self.type_vocab_size)

           _A					:     Optional[int]     = None
           _A					:     Any     = None
           if self.use_labels:
                  _A					:     Any     = ids_tensor([self.batch_size]				,							self.type_sequence_label_size)
                  _A					:     Optional[int]     = ids_tensor([self.batch_size, self.text_seq_length]				,							self.num_labels)

           _A					:     Optional[Any]     = LayoutLMvaConfig(
               vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							initializer_range=self.initializer_range				,							coordinate_size=self.coordinate_size				,							shape_size=self.shape_size				,							input_size=self.image_size				,							patch_size=self.patch_size				,							)

           return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Any):
           _A					:     Optional[int]     = TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE)

           # text + image
           _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE)
           _A					:     Any     = model(
               SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							attention_mask=SCREAMING_SNAKE_CASE				,							token_type_ids=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE				,							)
           _A					:     str     = model(SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE)

           self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size))

           # text only
           _A					:     Any     = model(SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.last_hidden_state.shape				,							(self.batch_size, self.text_seq_length, self.hidden_size))

           # image only
           _A					:     int     = model({'pixel_values': pixel_values}				,							training=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.last_hidden_state.shape				,							(self.batch_size, self.image_seq_length, self.hidden_size))


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           _A					:     Optional[Any]     = self.num_labels
           _A					:     List[Any]     = TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE)
           _A					:     int     = model(
               SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							attention_mask=SCREAMING_SNAKE_CASE				,							token_type_ids=SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE				,							)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels))


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           _A					:     List[str]     = self.num_labels
           _A					:     Optional[int]     = TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE)
           _A					:     int     = model(
               SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							attention_mask=SCREAMING_SNAKE_CASE				,							token_type_ids=SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE				,							)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.text_seq_length, self.num_labels))


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Tuple     = 2
           _A					:     int     = TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE)
           _A					:     Any     = model(
               SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							attention_mask=SCREAMING_SNAKE_CASE				,							token_type_ids=SCREAMING_SNAKE_CASE				,							start_positions=SCREAMING_SNAKE_CASE				,							end_positions=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE				,							)
           self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length))


    def        A   (  self      :							Optional[Any]):
           _A					:     Dict     = self.prepare_config_and_inputs()
           (_A)					:     List[str]     = config_and_inputs
           _A					:     List[str]     = {
               'input_ids': input_ids,
               'bbox': bbox,
               'pixel_values': pixel_values,
               'token_type_ids': token_type_ids,
               'attention_mask': input_mask,
           }
           return config, inputs_dict




@require_tf
class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    a													=   (
        {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							int):
           return True


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]=False):
           _A					:     int     = copy.deepcopy(SCREAMING_SNAKE_CASE)

           if model_class in get_values(SCREAMING_SNAKE_CASE):
                  _A					:     int     = {
                      k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE				,							1)				,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                      if isinstance(SCREAMING_SNAKE_CASE				,							tf.Tensor) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

           if return_labels:
                  if model_class in get_values(SCREAMING_SNAKE_CASE):
                         _A					:     List[str]     = tf.ones(self.model_tester.batch_size				,							dtype=tf.intaa)
                  elif model_class in get_values(SCREAMING_SNAKE_CASE):
                         _A					:     str     = tf.zeros(self.model_tester.batch_size				,							dtype=tf.intaa)
                         _A					:     Optional[int]     = tf.zeros(self.model_tester.batch_size				,							dtype=tf.intaa)
                  elif model_class in get_values(SCREAMING_SNAKE_CASE):
                         _A					:     Any     = tf.zeros(self.model_tester.batch_size				,							dtype=tf.intaa)
                  elif model_class in get_values(SCREAMING_SNAKE_CASE):
                         _A					:     str     = tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length)				,							dtype=tf.intaa)

           return inputs_dict


    def        A   (  self      :							Optional[int]):
           _A					:     str     = TFLayoutLMvaModelTester(self)
           _A					:     int     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							hidden_size=37)


    def        A   (  self      :							str):
           self.config_tester.run_common_tests()


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[str]     = self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  _A					:     Any     = model_class(SCREAMING_SNAKE_CASE)
                  if getattr(SCREAMING_SNAKE_CASE				,							'hf_compute_loss'				,							SCREAMING_SNAKE_CASE):
                         # The number of elements in the loss should be the same as the number of elements in the label
                         _A					:     Tuple     = self._prepare_for_class(inputs_dict.copy()				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)
                         _A					:     Dict     = prepared_for_class[
                             sorted(prepared_for_class.keys() - inputs_dict.keys()				,							reverse=SCREAMING_SNAKE_CASE)[0]
                         ]
                         _A					:     Dict     = added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         _A					:     Dict     = self._prepare_for_class(inputs_dict.copy()				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)
                         _A					:     Any     = prepared_for_class.pop('input_ids')

                         _A					:     Tuple     = model(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                         # Test that model correctly compute the loss when we mask some positions
                         _A					:     Tuple     = self._prepare_for_class(inputs_dict.copy()				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)
                         _A					:     List[Any]     = prepared_for_class.pop('input_ids')
                         if "labels" in prepared_for_class:
                                _A					:     Tuple     = prepared_for_class['labels'].numpy()
                                if len(labels.shape) > 1 and labels.shape[1] != 1:
                                       _A					:     str     = -100
                                       _A					:     Union[str, Any]     = tf.convert_to_tensor(SCREAMING_SNAKE_CASE)
                                       _A					:     Optional[Any]     = model(SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)[0]
                                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                                       self.assertTrue(not np.any(np.isnan(loss.numpy())))

                # Test that model correctly compute the loss with a dict
                         _A					:     Dict     = self._prepare_for_class(inputs_dict.copy()				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)
                         _A					:     int     = model(SCREAMING_SNAKE_CASE)[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                         # Test that model correctly compute the loss with a tuple
                         _A					:     Optional[int]     = self._prepare_for_class(inputs_dict.copy()				,							SCREAMING_SNAKE_CASE				,							return_labels=SCREAMING_SNAKE_CASE)

                         # Get keys that were added with the _prepare_for_class function
                         _A					:     Tuple     = prepared_for_class.keys() - inputs_dict.keys()
                         _A					:     str     = inspect.signature(model.call).parameters
                         _A					:     Tuple     = list(signature.keys())

                         # Create a dictionary holding the location of the tensors in the tuple
                         _A					:     Optional[Any]     = {0: 'input_ids'}
                         for label_key in label_keys:
                                _A					:     Tuple     = signature_names.index(SCREAMING_SNAKE_CASE)
                                _A					:     Optional[Any]     = label_key
                         _A					:     List[Any]     = sorted(tuple_index_mapping.items())
                         # Initialize a list with their default values, update the values and convert to a tuple
                         _A					:     Optional[Any]     = []

                         for name in signature_names:
                                if name != "kwargs":
                                       list_input.append(signature[name].default)

                         for index, value in sorted_tuple_index_mapping:
                                _A					:     List[str]     = prepared_for_class[value]

                         _A					:     Optional[Any]     = tuple(SCREAMING_SNAKE_CASE)

                         # Send to model
                         _A					:     Optional[Any]     = model(tuple_input[:-1])[0]

                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])


    def        A   (  self      :							Tuple):
           (
               _A			
           )					:     str     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]):
           (
               _A			
           )					:     Dict     = self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  _A					:     Optional[Any]     = type
                  self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           (
               _A			
           )					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]):
           (
               _A			
           )					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           (
               _A			
           )					:     List[str]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    @slow
    def        A   (  self      :							Union[str, Any]):
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Optional[Any]     = TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    ):
       _A					:     Optional[Any]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_tf
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							Union[str, Any]):
           return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE) if is_vision_available() else None


    @slow
    def        A   (  self      :							List[str]):
           _A					:     Tuple     = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base')

           _A					:     Tuple     = self.default_image_processor
           _A					:     Optional[int]     = prepare_img()
           _A					:     Dict     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='tf').pixel_values

           _A					:     int     = tf.constant([[1, 2]])
           _A					:     str     = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]])				,							axis=0)

           # forward pass
           _A					:     Tuple     = model(input_ids=SCREAMING_SNAKE_CASE				,							bbox=SCREAMING_SNAKE_CASE				,							pixel_values=SCREAMING_SNAKE_CASE				,							training=SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     Tuple     = (1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = tf.constant(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]])

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))










'''simple docstring'''







import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
   import tensorflow as tf

if is_torch_available():
   import torch


A				:					Any    						=			logging.get_logger(__name__)


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							uuid.UUID = None				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None):
           if not conversation_id:
                  _A					:     str     = uuid.uuida()
           if past_user_inputs is None:
                  _A					:     List[Any]     = []
           if generated_responses is None:
                  _A					:     Union[str, Any]     = []

           _A					:     uuid.UUID     = conversation_id
           _A					:     List[str]     = past_user_inputs
           _A					:     List[str]     = generated_responses
           _A					:     Optional[str]     = text


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return False
           if self.uuid == other.uuid:
                  return True
           return (
               self.new_user_input == other.new_user_input
               and self.past_user_inputs == other.past_user_inputs
               and self.generated_responses == other.generated_responses
           )


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							bool = False):
           if self.new_user_input:
                  if overwrite:
                         logger.warning(
                             F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                             F'with: "{text}".')
                         _A					:     Optional[Any]     = text
                  else:
                         logger.warning(
                             F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                             F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input')
           else:
                  _A					:     Optional[Any]     = text


    def        A   (  self      :							Optional[int]):
           if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input)
           _A					:     Optional[Any]     = None


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           self.generated_responses.append(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           for user_input, generated_response in zip(self.past_user_inputs				,							self.generated_responses):
                  yield True, user_input
                  yield False, generated_response
           if self.new_user_input:
                  yield True, self.new_user_input


    def __repr__(  self      :							Dict):
           _A					:     Any     = F'Conversation id: {self.uuid} \n'
           for is_user, text in self.iter_texts():
                  _A					:     Optional[int]     = 'user' if is_user else 'bot'
                  output += F'{name} >> {text} \n'
           return output




@add_end_docstrings(
    a_    , R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "    , )
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							str):
           super().__init__(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.tokenizer.pad_token_id is None:
                  _A					:     Any     = self.tokenizer.eos_token


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							**SCREAMING_SNAKE_CASE      :							str):
           _A					:     str     = {}
           _A					:     Union[str, Any]     = {}
           _A					:     List[str]     = {}

           if min_length_for_response is not None:
                  _A					:     Optional[Any]     = min_length_for_response
           if minimum_tokens is not None:
                  _A					:     Tuple     = minimum_tokens

           if "max_length" in generate_kwargs:
                  _A					:     List[Any]     = generate_kwargs['max_length']
                  # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
           if clean_up_tokenization_spaces is not None:
                  _A					:     List[str]     = clean_up_tokenization_spaces

           if generate_kwargs:
                  forward_params.update(SCREAMING_SNAKE_CASE)
           return preprocess_params, forward_params, postprocess_params


    def __call__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[Conversation, List[Conversation]]				,							SCREAMING_SNAKE_CASE      :							Any=0				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = super().__call__(SCREAMING_SNAKE_CASE				,							num_workers=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) and len(SCREAMING_SNAKE_CASE) == 1:
                  return outputs[0]
           return outputs


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Conversation				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=32):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise ValueError('ConversationalPipeline, expects Conversation as inputs')
           if conversation.new_user_input is None:
                  raise ValueError(
                      F'Conversation with UUID {type(conversation.uuid)} does not contain new user input to process. '
                      'Add user inputs with the conversation\'s `add_user_input` method')
           if hasattr(self.tokenizer				,							'_build_conversation_input_ids'):
                  _A					:     Optional[Any]     = self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE)
           else:
                  # If the tokenizer cannot handle conversations, we default to only the old version
                  _A					:     Dict     = self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE)

           if self.framework == "pt":
                  _A					:     Union[str, Any]     = torch.LongTensor([input_ids])
           elif self.framework == "tf":
                  _A					:     Optional[Any]     = tf.constant([input_ids])
           return {"input_ids": input_ids, "conversation": conversation}


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=10				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     str     = generate_kwargs.get('max_length'				,							self.model.config.max_length)

           _A					:     Any     = model_inputs['input_ids'].shape[1]
           if max_length - minimum_tokens < n:
                  logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})')
                  _A					:     Dict     = max_length - minimum_tokens
                  _A					:     int     = model_inputs['input_ids'][:, -trim:]
                  if "attention_mask" in model_inputs:
                         _A					:     str     = model_inputs['attention_mask'][:, -trim:]
           _A					:     Any     = model_inputs.pop('conversation')
           _A					:     Optional[Any]     = max_length
           _A					:     int     = self.model.generate(**SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.model.config.is_encoder_decoder:
                  _A					:     int     = 1
           else:
                  _A					:     List[Any]     = n
           return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]=True):
           _A					:     Optional[Any]     = model_outputs['output_ids']
           _A					:     Optional[int]     = self.tokenizer.decode(
               output_ids[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE				,							)
           _A					:     Any     = model_outputs['conversation']
           conversation.mark_processed()
           conversation.append_response(SCREAMING_SNAKE_CASE)
           return conversation


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Conversation):
           _A					:     Optional[Any]     = self.tokenizer.eos_token_id
           _A					:     List[Any]     = []
           for is_user, text in conversation.iter_texts():
                  if eos_token_id is not None:
                         input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE) + [eos_token_id])
                  else:
                         input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE))

           if len(SCREAMING_SNAKE_CASE) > self.tokenizer.model_max_length:
                  _A					:     Dict     = input_ids[-self.tokenizer.model_max_length :]
           return input_ids






