


'''simple docstring'''
from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
		from transformers import (
		    TFBertModel,
		    TFCLIPVisionModel,
		    TFDeiTModel,
		    TFRobertaModel,
		    TFVisionTextDualEncoderModel,
		    TFViTModel,
		    VisionTextDualEncoderConfig,
		)

if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor


def       a				(      __a       )  ->     Dict:



					'''simple docstring'''






					if isinstance(_lowerCamelCase			, collections.abc.Iterable       ):
										return x
					return (x, x)
@require_tf
class        lowercase     :

		"""simple docstring"""

		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase_   ,       UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			TFVisionTextDualEncoderModel(UpperCamelCase_							)

							UpperCamelCase__       :int							 =			model(input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_							)

							self.assertEqual(output['''text_embeds'''].shape   ,       (input_ids.shape[0], config.projection_dim)							)
							self.assertEqual(output['''image_embeds'''].shape   ,       (pixel_values.shape[0], config.projection_dim)							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.get_vision_text_model(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			TFVisionTextDualEncoderModel(vision_model=UpperCamelCase_   ,       text_model=UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			model(input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_							)

							self.assertEqual(output['''text_embeds'''].shape   ,       (input_ids.shape[0], model.config.projection_dim)							)
							self.assertEqual(output['''image_embeds'''].shape   ,       (pixel_values.shape[0], model.config.projection_dim)							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.get_vision_text_model(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			{"""vision_model""": vision_model, """text_model""": text_model}
							UpperCamelCase__       :int							 =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase_							)

							UpperCamelCase__       :Union[str, Any]							 =			model(input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_							)

							self.assertEqual(output['''text_embeds'''].shape   ,       (input_ids.shape[0], model.config.projection_dim)							)
							self.assertEqual(output['''image_embeds'''].shape   ,       (pixel_values.shape[0], model.config.projection_dim)							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.get_vision_text_model(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			TFVisionTextDualEncoderModel(vision_model=UpperCamelCase_   ,       text_model=UpperCamelCase_							)

							UpperCamelCase__       :Any							 =			model(input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			output[0].numpy()

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__       :str							 =			TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase_							)

												UpperCamelCase__       :Optional[int]							 =			model(input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_							)
												UpperCamelCase__       :int							 =			after_output[0].numpy()
												UpperCamelCase__       :Tuple							 =			np.amax(np.abs(out_a - out_a							)							)
												self.assertLessEqual(UpperCamelCase_   ,       1e-5							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.get_vision_text_model(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			TFVisionTextDualEncoderModel(vision_model=UpperCamelCase_   ,       text_model=UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(
							    input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_   ,       output_attentions=UpperCamelCase_							)

							UpperCamelCase__       :Optional[Any]							 =			output.vision_model_output.attentions
							self.assertEqual(len(UpperCamelCase_							)   ,       vision_config.num_hidden_layers							)

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							UpperCamelCase__       :Any							 =			to_atuple(vision_model.config.image_size							)
							UpperCamelCase__       :Tuple							 =			to_atuple(vision_model.config.patch_size							)
							UpperCamelCase__       :int							 =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							UpperCamelCase__       :Union[str, Any]							 =			num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]   ,       (vision_config.num_attention_heads, seq_len, seq_len)							)

							UpperCamelCase__       :Any							 =			output.text_model_output.attentions
							self.assertEqual(len(UpperCamelCase_							)   ,       text_config.num_hidden_layers							)

							self.assertEqual(
							    text_attentions[0].shape[-3:]   ,       (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,       )




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			np.abs((a - b)							).max()
							self.assertLessEqual(UpperCamelCase_   ,       UpperCamelCase_   ,       F'''Difference between torch and flax is {diff} (>= {tol}).'''							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_model(**UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.prepare_config_and_inputs()
							self.check_save_load(**UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**UpperCamelCase_							)




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.get_pretrained_model_and_inputs()

							UpperCamelCase__       :Any							 =			model_a(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			outputs[0].numpy()

							with tempfile.TemporaryDirectory() as tmp_dirname:
												model_a.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__       :List[str]							 =			TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase_							)

												UpperCamelCase__       :Optional[int]							 =			model_a(**UpperCamelCase_							)
												UpperCamelCase__       :Union[str, Any]							 =			after_outputs[0].numpy()
												UpperCamelCase__       :Dict							 =			np.amax(np.abs(out_a - out_a							)							)
												self.assertLessEqual(UpperCamelCase_   ,       1e-5							)



@require_tf
class        lowercase     (							__lowercase     ,		unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''hf-internal-testing/tiny-random-vit'''   ,       '''hf-internal-testing/tiny-random-bert'''							)
							UpperCamelCase__       :Tuple							 =			13
							UpperCamelCase__       :List[str]							 =			floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]							)
							UpperCamelCase__       :Union[str, Any]							 =			ids_tensor([batch_size, 4]   ,       model.text_model.config.vocab_size							)
							UpperCamelCase__       :Optional[Any]							 =			random_attention_mask([batch_size, 4]							)
							UpperCamelCase__       :str							 =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

							return model, inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			TFViTModel(UpperCamelCase_   ,       name='''vision_model'''							)
							UpperCamelCase__       :int							 =			TFBertModel(UpperCamelCase_   ,       name='''text_model'''							)
							return vision_model, text_model




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			TFViTModelTester(self							)
							UpperCamelCase__       :Optional[Any]							 =			TFBertModelTester(self							)
							UpperCamelCase__       :List[str]							 =			vit_model_tester.prepare_config_and_inputs()
							UpperCamelCase__       :Tuple							 =			bert_model_tester.prepare_config_and_inputs()

							UpperCamelCase__       :Optional[Any]							 =			vision_config_and_inputs

							(
							    UpperCamelCase__      
							)       :List[str]							 =			text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}



@require_tf
class        lowercase     (							__lowercase     ,		unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''Rocketknight1/tiny-random-deit-tf'''   ,       '''hf-internal-testing/tiny-random-roberta'''							)
							UpperCamelCase__       :int							 =			13
							UpperCamelCase__       :List[str]							 =			floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]							)
							UpperCamelCase__       :Any							 =			ids_tensor([batch_size, 4]   ,       model.text_model.config.vocab_size							)
							UpperCamelCase__       :Tuple							 =			random_attention_mask([batch_size, 4]							)
							UpperCamelCase__       :Optional[Any]							 =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

							return model, inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.get_vision_text_model(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :int							 =			TFVisionTextDualEncoderModel(vision_model=UpperCamelCase_   ,       text_model=UpperCamelCase_							)

							UpperCamelCase__       :Any							 =			model(
							    input_ids=UpperCamelCase_   ,       pixel_values=UpperCamelCase_   ,       attention_mask=UpperCamelCase_   ,       output_attentions=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			output.vision_model_output.attentions
							self.assertEqual(len(UpperCamelCase_							)   ,       vision_config.num_hidden_layers							)

							# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
							UpperCamelCase__       :Optional[int]							 =			to_atuple(vision_model.config.image_size							)
							UpperCamelCase__       :Optional[int]							 =			to_atuple(vision_model.config.patch_size							)
							UpperCamelCase__       :Union[str, Any]							 =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							UpperCamelCase__       :List[str]							 =			num_patches + 2
							self.assertEqual(vision_attentions[0].shape[-3:]   ,       (vision_config.num_attention_heads, seq_len, seq_len)							)

							UpperCamelCase__       :Union[str, Any]							 =			output.text_model_output.attentions
							self.assertEqual(len(UpperCamelCase_							)   ,       text_config.num_hidden_layers							)

							self.assertEqual(
							    text_attentions[0].shape[-3:]   ,       (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,       )




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			TFDeiTModel(UpperCamelCase_   ,       name='''vision_model'''							)
							UpperCamelCase__       :Optional[Any]							 =			TFRobertaModel(UpperCamelCase_   ,       name='''text_model'''							)
							return vision_model, text_model




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			TFDeiTModelTester(self							)
							UpperCamelCase__       :Optional[int]							 =			TFRobertaModelTester(self							)
							UpperCamelCase__       :Dict							 =			vit_model_tester.prepare_config_and_inputs()
							UpperCamelCase__       :Optional[int]							 =			bert_model_tester.prepare_config_and_inputs()

							UpperCamelCase__       :int							 =			vision_config_and_inputs

							(
							    UpperCamelCase__      
							)       :Optional[int]							 =			text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}



@require_tf
class        lowercase     (							__lowercase     ,		unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''Rocketknight1/tiny-random-clip-tf'''   ,       '''hf-internal-testing/tiny-random-bert'''							)
							UpperCamelCase__       :Optional[int]							 =			13
							UpperCamelCase__       :List[Any]							 =			floats_tensor(
							    [
							        batch_size,
							        model.vision_model.config.num_channels,
							        model.vision_model.config.image_size,
							        model.vision_model.config.image_size,
							    ]							)
							UpperCamelCase__       :int							 =			ids_tensor([batch_size, 4]   ,       model.text_model.config.vocab_size							)
							UpperCamelCase__       :Optional[int]							 =			random_attention_mask([batch_size, 4]							)
							UpperCamelCase__       :Dict							 =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

							return model, inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			TFCLIPVisionModel(UpperCamelCase_   ,       name='''vision_model'''							)
							UpperCamelCase__       :str							 =			TFBertModel(UpperCamelCase_   ,       name='''text_model'''							)
							return vision_model, text_model




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			TFCLIPVisionModelTester(self							)
							UpperCamelCase__       :Dict							 =			TFBertModelTester(self							)
							UpperCamelCase__       :str							 =			clip_model_tester.prepare_config_and_inputs()
							UpperCamelCase__       :Tuple							 =			bert_model_tester.prepare_config_and_inputs()

							UpperCamelCase__       :Optional[int]							 =			vision_config_and_inputs

							(
							    UpperCamelCase__      
							)       :List[Any]							 =			text_config_and_inputs

							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": input_mask,
							    "input_ids": input_ids,
							    "text_token_type_ids": token_type_ids,
							    "text_sequence_labels": sequence_labels,
							    "text_token_labels": token_labels,
							    "text_choice_labels": choice_labels,
							}



@require_vision
@require_tf
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			TFVisionTextDualEncoderModel.from_pretrained(
							    '''clip-italian/clip-italian'''   ,       logit_scale_init_value=1.0   ,       from_pt=UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''							)

							UpperCamelCase__       :List[Any]							 =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__       :Optional[int]							 =			processor(
							    text=['''una foto di un gatto''', '''una foto di un cane''']   ,       images=UpperCamelCase_   ,       padding=UpperCamelCase_   ,       return_tensors='''np'''							)

							UpperCamelCase__       :str							 =			model(**UpperCamelCase_							)

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape   ,       (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])							)
							self.assertEqual(
							    outputs.logits_per_text.shape   ,       (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])   ,       )

							UpperCamelCase__       :Optional[Any]							 =			np.array([[1.2284727, 0.3104122]]							)

							self.assertTrue(np.allclose(outputs.logits_per_image.numpy()   ,       UpperCamelCase_   ,       atol=1e-3							)							)



'''simple docstring'''
from argparse import ArgumentParser

from .env import EnvironmentCommand


def       a				(      )  ->     Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			ArgumentParser('''Diffusers CLI tool'''			, usage='''diffusers-cli <command> [<args>]'''       )
					UpperCamelCase__       :Union[str, Any]							 =			parser.add_subparsers(help='''diffusers-cli command helpers'''       )

					# Register commands
					EnvironmentCommand.register_subcommand(__a       )

					# Let's go
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										parser.print_help()
										exit(1       )

					# Run
					UpperCamelCase__       :Optional[int]							 =			args.func(__a       )
					service.run()


if __name__ == "__main__":
		main()