

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)







@is_pt_tf_cross_test
class  lowercase				(       unittest.TestCase			):




				@slow
				def     a							(					self       ):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModel.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModel.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForPreTraining.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForPreTraining.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForCausalLM.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		snake_case_				,    snake_case_							=    TFAutoModelForCausalLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForCausalLM.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		snake_case_				,    snake_case_							=    AutoModelForCausalLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelWithLMHead.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelWithLMHead.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForMaskedLM.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		snake_case_				,    snake_case_							=    TFAutoModelForMaskedLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForMaskedLM.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		snake_case_				,    snake_case_							=    AutoModelForMaskedLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForSeqaSeqLM.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		snake_case_				,    snake_case_							=    TFAutoModelForSeqaSeqLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForSeqaSeqLM.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		snake_case_				,    snake_case_							=    AutoModelForSeqaSeqLM.from_pretrained(
																		    snake_case      ,   output_loading_info=snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForSequenceClassification.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForSequenceClassification.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				@slow
				def     a							(					self       ):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		snake_case_							=    AutoConfig.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    TFAutoModelForQuestionAnswering.from_pretrained(snake_case      ,   from_pt=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )

																		snake_case_							=    AutoModelForQuestionAnswering.from_pretrained(snake_case      ,   from_tf=snake_case       )
																		self.assertIsNotNone(snake_case       )
																		self.assertIsInstance(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    TFAutoModelWithLMHead.from_pretrained(snake_case      ,   from_pt=snake_case       )
											self.assertIsInstance(snake_case      ,   snake_case       )
											self.assertEqual(model.num_parameters()      ,   1_4410       )
											self.assertEqual(model.num_parameters(only_trainable=snake_case       )      ,   1_4410       )

											snake_case_							=    AutoModelWithLMHead.from_pretrained(snake_case      ,   from_tf=snake_case       )
											self.assertIsInstance(snake_case      ,   snake_case       )
											self.assertEqual(model.num_parameters()      ,   1_4410       )
											self.assertEqual(model.num_parameters(only_trainable=snake_case       )      ,   1_4410       )




				def     a							(					self       ):
											snake_case_							=    TFAutoModelWithLMHead.from_pretrained(snake_case      ,   from_pt=snake_case       )
											self.assertIsInstance(snake_case      ,   snake_case       )
											self.assertEqual(model.num_parameters()      ,   1_4410       )
											self.assertEqual(model.num_parameters(only_trainable=snake_case       )      ,   1_4410       )

											snake_case_							=    AutoModelWithLMHead.from_pretrained(snake_case      ,   from_tf=snake_case       )
											self.assertIsInstance(snake_case      ,   snake_case       )
											self.assertEqual(model.num_parameters()      ,   1_4410       )
											self.assertEqual(model.num_parameters(only_trainable=snake_case       )      ,   1_4410       )







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




