'''simple docstring'''



import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
	from transformers import (
	    FlaxBertModel,
	    FlaxCLIPVisionModel,
	    FlaxVisionTextDualEncoderModel,
	    FlaxViTModel,
	    VisionTextDualEncoderConfig,
	    VisionTextDualEncoderProcessor,
	)
	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)


if is_torch_available():
	import torch

	from transformers import VisionTextDualEncoderModel

if is_vision_available():
	from PIL import Image




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			if isinstance(lowerCamelCase_						,    collections.abc.Iterable			):
						return x
			return (x, x)






@require_flax
class 							__lowercase    :







	'''simple docstring'''

	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				pass


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







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							np.abs((a - b)	).max()
				self.assertLessEqual(_lowerCamelCase       ,_lowerCamelCase       ,f"Difference between torch and flax is {diff} (>= {tol})."	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None       ,**_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							FlaxVisionTextDualEncoderModel(_lowerCamelCase	)

				__lowercase			  =							model(input_ids=_lowerCamelCase       ,pixel_values=_lowerCamelCase       ,attention_mask=_lowerCamelCase	)

				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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None       ,**_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.get_vision_text_model(_lowerCamelCase       ,_lowerCamelCase	)
				__lowercase			  =							{'''vision_model''': vision_model, '''text_model''': text_model}
				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase	)

				__lowercase			  =							model(input_ids=_lowerCamelCase       ,pixel_values=_lowerCamelCase       ,attention_mask=_lowerCamelCase	)

				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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None       ,**_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.get_vision_text_model(_lowerCamelCase       ,_lowerCamelCase	)
				__lowercase			  =							{'''vision_model''': vision_model, '''text_model''': text_model}
				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase	)

				__lowercase			  =							model(input_ids=_lowerCamelCase       ,pixel_values=_lowerCamelCase       ,attention_mask=_lowerCamelCase	)
				__lowercase			  =							output[0]

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(_lowerCamelCase	)
							__lowercase			  =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase	)

							__lowercase			  =							model(input_ids=_lowerCamelCase       ,pixel_values=_lowerCamelCase       ,attention_mask=_lowerCamelCase	)
							__lowercase			  =							after_output[0]
							__lowercase			  =							np.amax(np.abs(out_a - out_a	)	)
							self.assertLessEqual(_lowerCamelCase       ,1E-3	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None       ,**_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.get_vision_text_model(_lowerCamelCase       ,_lowerCamelCase	)
				__lowercase			  =							{'''vision_model''': vision_model, '''text_model''': text_model}
				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase	)

				__lowercase			  =							model(
				    input_ids=_lowerCamelCase       ,pixel_values=_lowerCamelCase       ,attention_mask=_lowerCamelCase       ,output_attentions=_lowerCamelCase	)

				__lowercase			  =							output.vision_model_output.attentions
				self.assertEqual(len(_lowerCamelCase	)       ,vision_config.num_hidden_layers	)

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

				__lowercase			  =							output.text_model_output.attentions
				self.assertEqual(len(_lowerCamelCase	)       ,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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				pt_model.to(_lowerCamelCase	)
				pt_model.eval()

				# prepare inputs
				__lowercase			  =							inputs_dict
				__lowercase			  =							{k: torch.tensor(v.tolist()	) for k, v in flax_inputs.items()}

				with torch.no_grad():
							__lowercase			  =							pt_model(**_lowerCamelCase	).to_tuple()

				__lowercase			  =							fx_model(**_lowerCamelCase	).to_tuple()
				self.assertEqual(len(_lowerCamelCase	)       ,len(_lowerCamelCase	)       ,'''Output lengths differ between Flax and PyTorch'''	)
				for fx_output, pt_output in zip(fx_outputs[:4]       ,pt_outputs[:4]	):
							self.assert_almost_equals(_lowerCamelCase       ,pt_output.numpy()       ,4E-2	)

				# PT -> Flax
				with tempfile.TemporaryDirectory() as tmpdirname:
							pt_model.save_pretrained(_lowerCamelCase	)
							__lowercase			  =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase       ,from_pt=_lowerCamelCase	)

				__lowercase			  =							fx_model_loaded(**_lowerCamelCase	).to_tuple()
				self.assertEqual(len(_lowerCamelCase	)       ,len(_lowerCamelCase	)       ,'''Output lengths differ between Flax and PyTorch'''	)
				for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]       ,pt_outputs[:4]	):
							self.assert_almost_equals(_lowerCamelCase       ,pt_output.numpy()       ,4E-2	)

				# Flax -> PT
				with tempfile.TemporaryDirectory() as tmpdirname:
							fx_model.save_pretrained(_lowerCamelCase	)
							__lowercase			  =							VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase       ,from_flax=_lowerCamelCase	)

				pt_model_loaded.to(_lowerCamelCase	)
				pt_model_loaded.eval()

				with torch.no_grad():
							__lowercase			  =							pt_model_loaded(**_lowerCamelCase	).to_tuple()

				self.assertEqual(len(_lowerCamelCase	)       ,len(_lowerCamelCase	)       ,'''Output lengths differ between Flax and PyTorch'''	)
				for fx_output, pt_output_loaded in zip(fx_outputs[:4]       ,pt_outputs_loaded[:4]	):
							self.assert_almost_equals(_lowerCamelCase       ,pt_output_loaded.numpy()       ,4E-2	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							VisionTextDualEncoderModel(_lowerCamelCase	)
				__lowercase			  =							FlaxVisionTextDualEncoderModel(_lowerCamelCase	)

				__lowercase			  =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,_lowerCamelCase	)
				__lowercase			  =							fx_state

				self.check_pt_flax_equivalence(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							VisionTextDualEncoderModel(_lowerCamelCase	)
				__lowercase			  =							FlaxVisionTextDualEncoderModel(_lowerCamelCase	)

				__lowercase			  =							load_flax_weights_in_pytorch_model(_lowerCamelCase       ,fx_model.params	)

				self.check_pt_flax_equivalence(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase	)


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







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				self.check_save_load(**_lowerCamelCase	)


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







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**_lowerCamelCase	)


	@is_pt_flax_cross_test
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.prepare_config_and_inputs()
				__lowercase			  =							config_inputs_dict.pop('''vision_config'''	)
				__lowercase			  =							config_inputs_dict.pop('''text_config'''	)

				__lowercase			  =							config_inputs_dict

				self.check_equivalence_pt_to_flax(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)
				self.check_equivalence_flax_to_pt(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)


	@slow
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.get_pretrained_model_and_inputs()

				__lowercase			  =							model_a(**_lowerCamelCase	)
				__lowercase			  =							outputs[0]

				with tempfile.TemporaryDirectory() as tmp_dirname:
							model_a.save_pretrained(_lowerCamelCase	)
							__lowercase			  =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase	)

							__lowercase			  =							model_a(**_lowerCamelCase	)
							__lowercase			  =							after_outputs[0]
							__lowercase			  =							np.amax(np.abs(out_a - out_a	)	)
							self.assertLessEqual(_lowerCamelCase       ,1E-5	)






@require_flax
class 							__lowercase    ( lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    '''hf-internal-testing/tiny-random-vit'''       ,'''hf-internal-testing/tiny-bert'''       ,vision_from_pt=_lowerCamelCase       ,text_from_pt=_lowerCamelCase       ,)
				__lowercase			  =							13
				__lowercase			  =							floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]	)
				__lowercase			  =							ids_tensor([batch_size, 4]       ,model.config.text_config.vocab_size	)
				__lowercase			  =							random_attention_mask([batch_size, 4]	)
				__lowercase			  =							{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

				return model, inputs


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							FlaxViTModel(_lowerCamelCase	)
				__lowercase			  =							FlaxBertModel(_lowerCamelCase	)
				return vision_model, text_model


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							FlaxViTModelTester(self	)
				__lowercase			  =							FlaxBertModelTester(self	)
				__lowercase			  =							vit_model_tester.prepare_config_and_inputs()
				__lowercase			  =							bert_model_tester.prepare_config_and_inputs()

				__lowercase  ,						__lowercase			  =							vision_config_and_inputs

				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}






@require_torch
class 							__lowercase    ( lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    '''hf-internal-testing/tiny-random-clip'''       ,'''hf-internal-testing/tiny-bert'''       ,vision_from_pt=_lowerCamelCase       ,text_from_pt=_lowerCamelCase       ,)
				__lowercase			  =							13
				__lowercase			  =							floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]	)
				__lowercase			  =							ids_tensor([batch_size, 4]       ,model.config.text_config.vocab_size	)
				__lowercase			  =							random_attention_mask([batch_size, 4]	)
				__lowercase			  =							{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

				return model, inputs


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							FlaxCLIPVisionModel(_lowerCamelCase	)
				__lowercase			  =							FlaxBertModel(_lowerCamelCase	)
				return vision_model, text_model


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							FlaxCLIPVisionModelTester(self	)
				__lowercase			  =							FlaxBertModelTester(self	)
				__lowercase			  =							clip_model_tester.prepare_config_and_inputs()
				__lowercase			  =							bert_model_tester.prepare_config_and_inputs()

				__lowercase  ,						__lowercase			  =							vision_config_and_inputs

				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}






@require_flax
@require_vision
class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	@slow
	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''       ,logit_scale_init_value=1.0	)
				__lowercase			  =							VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''	)

				__lowercase			  =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
				__lowercase			  =							processor(
				    text=['''una foto di un gatto''', '''una foto di un cane''']       ,images=_lowerCamelCase       ,padding=_lowerCamelCase       ,return_tensors='''np'''	)

				__lowercase			  =							model(**_lowerCamelCase	)

				# 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])       ,)

				__lowercase			  =							np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]	)

				self.assertTrue(np.allclose(outputs.logits_per_image       ,_lowerCamelCase       ,atol=1E-3	)	)












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4





