

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.layoutlm.modeling_tf_layoutlm import (
					    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFLayoutLMForMaskedLM,
					    TFLayoutLMForQuestionAnswering,
					    TFLayoutLMForSequenceClassification,
					    TFLayoutLMForTokenClassification,
					    TFLayoutLMModel,
					)







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=99				,       snake_case=32				,       snake_case=2				,       snake_case=4				,       snake_case=37				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=16				,       snake_case=2				,       snake_case=0.02				,       snake_case=3				,       snake_case=4				,       snake_case=None				,       snake_case=1000				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_mask
											snake_case_          =   use_token_type_ids
											snake_case_          =   use_labels
											snake_case_          =   vocab_size
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_vocab_size
											snake_case_          =   type_sequence_label_size
											snake_case_          =   initializer_range
											snake_case_          =   num_labels
											snake_case_          =   num_choices
											snake_case_          =   scope
											snake_case_          =   range_bbox




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)

											# convert bbox to numpy since TF does not support item assignment
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length, 4]				,       self.range_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]:
																																snake_case_          =   bbox[i, j, 3]
																																snake_case_          =   bbox[i, j, 1]
																																snake_case_          =   t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																snake_case_          =   bbox[i, j, 2]
																																snake_case_          =   bbox[i, j, 0]
																																snake_case_          =   t
											snake_case_          =   tf.convert_to_tensor(snake_case	)

											snake_case_          =   None
											if self.use_input_mask:
																		snake_case_          =   random_attention_mask([self.batch_size, self.seq_length]	)

											snake_case_          =   None
											if self.use_token_type_ids:
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size	)

											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None
											if self.use_labels:
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.type_sequence_label_size	)
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels	)
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.num_choices	)

											snake_case_          =   LayoutLMConfig(
											    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				,       )

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




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   TFLayoutLMModel(config=snake_case	)

											snake_case_          =   model(snake_case				,       snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case	)
											snake_case_          =   model(snake_case				,       snake_case				,       token_type_ids=snake_case	)
											snake_case_          =   model(snake_case				,       snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)	)
											self.parent.assertEqual(result.pooler_output.shape				,       (self.batch_size, self.hidden_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   TFLayoutLMForMaskedLM(config=snake_case	)

											snake_case_          =   model(snake_case				,       snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   self.num_labels
											snake_case_          =   TFLayoutLMForSequenceClassification(config=snake_case	)

											snake_case_          =   model(snake_case				,       snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   self.num_labels
											snake_case_          =   TFLayoutLMForTokenClassification(config=snake_case	)

											snake_case_          =   model(snake_case				,       snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   TFLayoutLMForQuestionAnswering(config=snake_case	)

											snake_case_          =   model(snake_case				,       snake_case				,       attention_mask=snake_case				,       token_type_ids=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	):
											snake_case_          =   self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs
											snake_case_          =   {
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict







@require_tf
class 			lowercase    (	lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      (
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (
				    {
				        '''feature-extraction''': TFLayoutLMModel,
				        '''fill-mask''': TFLayoutLMForMaskedLM,
				        '''text-classification''': TFLayoutLMForSequenceClassification,
				        '''token-classification''': TFLayoutLMForTokenClassification,
				        '''zero-shot''': TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      False
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			Any	      =      10




				def    a							( self	):
											snake_case_          =   TFLayoutLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       hidden_size=37	)




				def    a							( self	):
											self.config_tester.run_common_tests()




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*snake_case	)




				@slow
				def    a							( self	):
											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   TFLayoutLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)




				@unittest.skip('Onnx compliancy broke with TF 2.10'	)
				def    a							( self	):
											pass





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]				)  # noqa: E231
							snake_case_          =   tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]				)  # noqa: E231
							snake_case_          =   tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]				)  # noqa: E231
							snake_case_          =   tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]				)  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							snake_case_          =   tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]				)  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	)

											snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   prepare_layoutlm_batch_inputs()

											# forward pass
											snake_case_          =   model(input_ids=snake_case				,       bbox=snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case	)

											# test the sequence output on [0, :3, :3]
											snake_case_          =   tf.convert_to_tensor(
											    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]				,       )

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

											# test the pooled output on [1, :3]
											snake_case_          =   tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]	)

											self.assertTrue(np.allclose(outputs.pooler_output[1, :3]				,       snake_case				,       atol=1e-3	)	)




				@slow
				def    a							( self	):
											# initialize model with randomly initialized sequence classification head
											snake_case_          =   TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=2	)

											snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   prepare_layoutlm_batch_inputs()

											# forward pass
											snake_case_          =   model(
											    input_ids=snake_case				,       bbox=snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=tf.convert_to_tensor([1, 1]	)				,       )

											# test whether we get a loss as a scalar
											snake_case_          =   outputs.loss
											snake_case_          =   (2,)
											self.assertEqual(loss.shape				,       snake_case	)

											# test the shape of the logits
											snake_case_          =   outputs.logits
											snake_case_          =   (2, 2)
											self.assertEqual(logits.shape				,       snake_case	)




				@slow
				def    a							( self	):
											# initialize model with randomly initialized token classification head
											snake_case_          =   TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=13	)

											snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   prepare_layoutlm_batch_inputs()

											# forward pass
											snake_case_          =   model(
											    input_ids=snake_case				,       bbox=snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)

											# test the shape of the logits
											snake_case_          =   outputs.logits
											snake_case_          =   tf.convert_to_tensor((2, 25, 13)	)
											self.assertEqual(logits.shape				,       snake_case	)




				@slow
				def    a							( self	):
											# initialize model with randomly initialized token classification head
											snake_case_          =   TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	)

											snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_          =   prepare_layoutlm_batch_inputs()

											# forward pass
											snake_case_          =   model(input_ids=snake_case				,       bbox=snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case	)

											# test the shape of the logits
											snake_case_          =   tf.convert_to_tensor((2, 25)	)
											self.assertEqual(outputs.start_logits.shape				,       snake_case	)
											self.assertEqual(outputs.end_logits.shape				,       snake_case	)







import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-canny'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'bird'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
											snake_case_          =   pipe.prepare_image_inputs([canny_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'Chef in the kitchen'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
											snake_case_          =   pipe.prepare_image_inputs([pose_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




