






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      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				if isinstance(_A      ,   collections.abc.Iterable  ):
								return x
				return (x, x)


@require_flax
class   __SCREAMING_SNAKE_CASE:
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[Any]						)		->		Optional[int]:
											pass
							def lowerCAmelCase_ (     self:      str						)		->		Tuple:
											pass
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											pass
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      float						)		->		List[str]:
											snake_case__									=     np.abs((a - b)						).max()
											self.assertLessEqual(UpperCamelCase       ,	UpperCamelCase       ,	F'''Difference between torch and flax is {diff} (>= {tol}).'''						)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Dict=None       ,	**UpperCamelCase:      Union[str, Any]						)		->		Union[str, Any]:
											snake_case__									=     VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     FlaxVisionTextDualEncoderModel(UpperCamelCase						)

											snake_case__									=     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:      List[Any]       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Union[str, Any]=None       ,	**UpperCamelCase:      Union[str, Any]						)		->		int:
											snake_case__							,						snake_case__									=     self.get_vision_text_model(UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     {'vision_model': vision_model, 'text_model': text_model}
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase						)

											snake_case__									=     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:      Optional[int]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Any=None       ,	**UpperCamelCase:      Tuple						)		->		Optional[Any]:
											snake_case__							,						snake_case__									=     self.get_vision_text_model(UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     {'vision_model': vision_model, 'text_model': text_model}
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase						)

											snake_case__									=     model(input_ids=UpperCamelCase       ,	pixel_values=UpperCamelCase       ,	attention_mask=UpperCamelCase						)
											snake_case__									=     output[0]

											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(UpperCamelCase						)
															snake_case__									=     FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase						)

															snake_case__									=     model(input_ids=UpperCamelCase       ,	pixel_values=UpperCamelCase       ,	attention_mask=UpperCamelCase						)
															snake_case__									=     after_output[0]
															snake_case__									=     np.amax(np.abs(out_a - out_a						)						)
															self.assertLessEqual(UpperCamelCase       ,	1e-3						)
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      List[str]=None       ,	**UpperCamelCase:      Optional[Any]						)		->		Dict:
											snake_case__							,						snake_case__									=     self.get_vision_text_model(UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     {'vision_model': vision_model, 'text_model': text_model}
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase						)

											snake_case__									=     model(
											    input_ids=UpperCamelCase       ,	pixel_values=UpperCamelCase       ,	attention_mask=UpperCamelCase       ,	output_attentions=UpperCamelCase						)

											snake_case__									=     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)
											snake_case__									=     to_atuple(vision_model.config.image_size						)
											snake_case__									=     to_atuple(vision_model.config.patch_size						)
											snake_case__									=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
											snake_case__									=     num_patches + 1
											self.assertEqual(vision_attentions[0].shape[-3:]       ,	(vision_config.num_attention_heads, seq_len, seq_len)						)

											snake_case__									=     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:      Optional[Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Optional[int]						)		->		Any:
											pt_model.to(UpperCamelCase						)
											pt_model.eval()

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

											with torch.no_grad():
															snake_case__									=     pt_model(**UpperCamelCase						).to_tuple()

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

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

											snake_case__									=     fx_model_loaded(**UpperCamelCase						).to_tuple()
											self.assertEqual(len(UpperCamelCase						)       ,	len(UpperCamelCase						)       ,	'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(UpperCamelCase       ,	pt_output.numpy()       ,	4e-2						)

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

											pt_model_loaded.to(UpperCamelCase						)
											pt_model_loaded.eval()

											with torch.no_grad():
															snake_case__									=     pt_model_loaded(**UpperCamelCase						).to_tuple()

											self.assertEqual(len(UpperCamelCase						)       ,	len(UpperCamelCase						)       ,	'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(UpperCamelCase       ,	pt_output_loaded.numpy()       ,	4e-2						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Any						)		->		List[str]:
											snake_case__									=     VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     VisionTextDualEncoderModel(UpperCamelCase						)
											snake_case__									=     FlaxVisionTextDualEncoderModel(UpperCamelCase						)

											snake_case__									=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	UpperCamelCase						)
											snake_case__									=     fx_state

											self.check_pt_flax_equivalence(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[Any]						)		->		Tuple:
											snake_case__									=     VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     VisionTextDualEncoderModel(UpperCamelCase						)
											snake_case__									=     FlaxVisionTextDualEncoderModel(UpperCamelCase						)

											snake_case__									=     load_flax_weights_in_pytorch_model(UpperCamelCase       ,	fx_model.params						)

											self.check_pt_flax_equivalence(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Tuple:
											snake_case__									=     self.prepare_config_and_inputs()
											self.check_model_from_pretrained_configs(**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Any:
											snake_case__									=     self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											snake_case__									=     self.prepare_config_and_inputs()
											self.check_save_load(**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											snake_case__									=     self.prepare_config_and_inputs()
											self.check_vision_text_output_attention(**UpperCamelCase						)
							@is_pt_flax_cross_test
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											snake_case__									=     self.prepare_config_and_inputs()
											snake_case__									=     config_inputs_dict.pop('vision_config'						)
											snake_case__									=     config_inputs_dict.pop('text_config'						)

											snake_case__									=     config_inputs_dict

											self.check_equivalence_pt_to_flax(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
											self.check_equivalence_flax_to_pt(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)


							@slow
							def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											snake_case__							,						snake_case__									=     self.get_pretrained_model_and_inputs()

											snake_case__									=     model_a(**UpperCamelCase						)
											snake_case__									=     outputs[0]

											with tempfile.TemporaryDirectory() as tmp_dirname:
															model_a.save_pretrained(UpperCamelCase						)
															snake_case__									=     FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase						)

															snake_case__									=     model_a(**UpperCamelCase						)
															snake_case__									=     after_outputs[0]
															snake_case__									=     np.amax(np.abs(out_a - out_a						)						)
															self.assertLessEqual(UpperCamelCase       ,	1e-5						)


@require_flax
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							def lowerCAmelCase_ (     self:      str						)		->		Optional[Any]:
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
											    'hf-internal-testing/tiny-random-vit'       ,	'hf-internal-testing/tiny-bert'       ,	vision_from_pt=UpperCamelCase       ,	text_from_pt=UpperCamelCase       ,	)
											snake_case__									=     13
											snake_case__									=     floats_tensor(
											    [
											        batch_size,
											        model.config.vision_config.num_channels,
											        model.config.vision_config.image_size,
											        model.config.vision_config.image_size,
											    ]						)
											snake_case__									=     ids_tensor([batch_size, 4]       ,	model.config.text_config.vocab_size						)
											snake_case__									=     random_attention_mask([batch_size, 4]						)
											snake_case__									=     {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

											return model, inputs
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[Any]						)		->		Union[str, Any]:
											snake_case__									=     FlaxViTModel(UpperCamelCase						)
											snake_case__									=     FlaxBertModel(UpperCamelCase						)
											return vision_model, text_model


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     FlaxViTModelTester(self						)
											snake_case__									=     FlaxBertModelTester(self						)
											snake_case__									=     vit_model_tester.prepare_config_and_inputs()
											snake_case__									=     bert_model_tester.prepare_config_and_inputs()

											snake_case__							,						snake_case__									=     vision_config_and_inputs

											snake_case__							,						snake_case__							,						snake_case__							,						snake_case__									=     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   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[str]:
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
											    'hf-internal-testing/tiny-random-clip'       ,	'hf-internal-testing/tiny-bert'       ,	vision_from_pt=UpperCamelCase       ,	text_from_pt=UpperCamelCase       ,	)
											snake_case__									=     13
											snake_case__									=     floats_tensor(
											    [
											        batch_size,
											        model.config.vision_config.num_channels,
											        model.config.vision_config.image_size,
											        model.config.vision_config.image_size,
											    ]						)
											snake_case__									=     ids_tensor([batch_size, 4]       ,	model.config.text_config.vocab_size						)
											snake_case__									=     random_attention_mask([batch_size, 4]						)
											snake_case__									=     {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

											return model, inputs
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      Tuple						)		->		List[str]:
											snake_case__									=     FlaxCLIPVisionModel(UpperCamelCase						)
											snake_case__									=     FlaxBertModel(UpperCamelCase						)
											return vision_model, text_model


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     FlaxCLIPVisionModelTester(self						)
											snake_case__									=     FlaxBertModelTester(self						)
											snake_case__									=     clip_model_tester.prepare_config_and_inputs()
											snake_case__									=     bert_model_tester.prepare_config_and_inputs()

											snake_case__							,						snake_case__									=     vision_config_and_inputs

											snake_case__							,						snake_case__							,						snake_case__							,						snake_case__									=     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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@slow
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'       ,	logit_scale_init_value=1.0						)
											snake_case__									=     VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'						)

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

											snake_case__									=     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])       ,	)

											snake_case__									=     np.array([[1.2_284_727, 0.3_104_122]]						)

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












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




