




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


UpperCamelCase								=					logging.get_logger(__name__)





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str = None       ,     SCREAMING_SNAKE_CASE__					:					uuid.UUID = None       ,     SCREAMING_SNAKE_CASE__					:					int=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=None       )				->	Optional[Any]:
         if not conversation_id:
              lowerCAmelCase__          =			uuid.uuida()
         if past_user_inputs is None:
              lowerCAmelCase__          =			[]
         if generated_responses is None:
              lowerCAmelCase__          =			[]

         lowerCAmelCase__          =			conversation_id
         lowerCAmelCase__          =			past_user_inputs
         lowerCAmelCase__          =			generated_responses
         lowerCAmelCase__          =			text


    def __eq__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Dict:
         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					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	Tuple:
         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}".'       )
                   lowerCAmelCase__          =			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:
              lowerCAmelCase__          =			text


    def        a				(	self					:					str       )				->	List[str]:
         if self.new_user_input:
              self.past_user_inputs.append(self.new_user_input       )
         lowerCAmelCase__          =			None


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Union[str, Any]:
         self.generated_responses.append(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         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					:					Tuple       )				->	List[str]:
         lowerCAmelCase__          =			f'Conversation id: {self.uuid} \n'
         for is_user, text in self.iter_texts():
              lowerCAmelCase__          =			"user" if is_user else "bot"
              output += f'{name} >> {text} \n'
         return output






@add_end_docstrings(
    UpperCamelCase__     ,    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	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	List[Any]:
         super().__init__(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         if self.tokenizer.pad_token_id is None:
              lowerCAmelCase__          =			self.tokenizer.eos_token


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Tuple=None       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			{}
         lowerCAmelCase__          =			{}
         lowerCAmelCase__          =			{}

         if min_length_for_response is not None:
              lowerCAmelCase__          =			min_length_for_response
         if minimum_tokens is not None:
              lowerCAmelCase__          =			minimum_tokens

         if "max_length" in generate_kwargs:
              lowerCAmelCase__          =			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:
              lowerCAmelCase__          =			clean_up_tokenization_spaces

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


    def __call__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[Conversation, List[Conversation]]       ,     SCREAMING_SNAKE_CASE__					:					List[str]=0       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			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					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Conversation       ,     SCREAMING_SNAKE_CASE__					:					int=32       )				->	Dict[str, Any]:
         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"       ):
              lowerCAmelCase__          =			self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE__       )
         else:
              # If the tokenizer cannot handle conversations, we default to only the old version
              lowerCAmelCase__          =			self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE__       )

         if self.framework == "pt":
              lowerCAmelCase__          =			torch.LongTensor([input_ids]       )
         elif self.framework == "tf":
              lowerCAmelCase__          =			tf.constant([input_ids]       )
         return {"input_ids": input_ids, "conversation": conversation}


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Dict=10       ,     **SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
         lowerCAmelCase__          =			generate_kwargs.get("max_length"       ,     self.model.config.max_length       )

         lowerCAmelCase__          =			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})'       )
              lowerCAmelCase__          =			max_length - minimum_tokens
              lowerCAmelCase__          =			model_inputs["input_ids"][:, -trim:]
              if "attention_mask" in model_inputs:
                   lowerCAmelCase__          =			model_inputs["attention_mask"][:, -trim:]
         lowerCAmelCase__          =			model_inputs.pop("conversation"       )
         lowerCAmelCase__          =			max_length
         lowerCAmelCase__          =			self.model.generate(**SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         if self.model.config.is_encoder_decoder:
              lowerCAmelCase__          =			1
         else:
              lowerCAmelCase__          =			n
         return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any=True       )				->	List[str]:
         lowerCAmelCase__          =			model_outputs["output_ids"]
         lowerCAmelCase__          =			self.tokenizer.decode(
             output_ids[0]       ,     skip_special_tokens=SCREAMING_SNAKE_CASE__       ,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			model_outputs["conversation"]
         conversation.mark_processed()
         conversation.append_response(SCREAMING_SNAKE_CASE__       )
         return conversation


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Conversation       )				->	Dict:
         lowerCAmelCase__          =			self.tokenizer.eos_token_id
         lowerCAmelCase__          =			[]
         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:
              lowerCAmelCase__          =			input_ids[-self.tokenizer.model_max_length :]
         return input_ids






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

         lowerCAmelCase__          =			BlipImageProcessor()
         lowerCAmelCase__          =			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"       )

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta       )]

         lowerCAmelCase__          =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,     0       ,     -1       )       ) for x in image_inputs]

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         lowerCAmelCase__          =			self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"       )
         lowerCAmelCase__          =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         lowerCAmelCase__          =			BlipaProcessor.from_pretrained(
             self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE__       )

         self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )

         # test if it raises when no input is passed
         with pytest.raises(SCREAMING_SNAKE_CASE__       ):
              processor()


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )
