



"""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 __lowercase    (							_a					):
    if isinstance(_a	,    collections.abc.Iterable					):
        return x
    return (x, x)




@require_flax
class  _UpperCAmelCase :

 def 		_snake_case      (  self	: List[Any]						,  lowercase_	: Optional[Any]						,  lowercase_	: Any       ):
     pass

 def 		_snake_case      (  self	: Optional[Any]       ):
     pass

 def 		_snake_case      (  self	: List[str]       ):
     pass

 def 		_snake_case      (  self	: Any						,  lowercase_	: np.ndarray						,  lowercase_	: np.ndarray						,  lowercase_	: float       ):
     snake_case_				:  List[str]       =			np.abs((a - b)       ).max()
     self.assertLessEqual(lowercase_						,  lowercase_						,  f"Difference between torch and flax is {diff} (>= {tol})."       )

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: Optional[int]						,  lowercase_	: Dict						,  lowercase_	: str						,  lowercase_	: List[Any]=None						,  **lowercase_	: str       ):
     snake_case_				:  Tuple       =			VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_						,  lowercase_       )

     snake_case_				:  Union[str, Any]       =			FlaxVisionTextDualEncoderModel(lowercase_       )

     snake_case_				:  Dict       =			model(input_ids=lowercase_						,  pixel_values=lowercase_						,  attention_mask=lowercase_       )

     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 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[str]						,  lowercase_	: List[Any]						,  lowercase_	: int						,  lowercase_	: Any						,  lowercase_	: Any=None						,  **lowercase_	: List[str]       ):
     snake_case_,			snake_case_				:  Tuple       =			self.get_vision_text_model(lowercase_						,  lowercase_       )
     snake_case_				:  str       =			{'''vision_model''': vision_model, '''text_model''': text_model}
     snake_case_				:  Optional[int]       =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_       )

     snake_case_				:  Union[str, Any]       =			model(input_ids=lowercase_						,  pixel_values=lowercase_						,  attention_mask=lowercase_       )

     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 		_snake_case      (  self	: int						,  lowercase_	: List[Any]						,  lowercase_	: str						,  lowercase_	: Any						,  lowercase_	: Union[str, Any]						,  lowercase_	: Optional[int]=None						,  **lowercase_	: Dict       ):
     snake_case_,			snake_case_				:  Tuple       =			self.get_vision_text_model(lowercase_						,  lowercase_       )
     snake_case_				:  int       =			{'''vision_model''': vision_model, '''text_model''': text_model}
     snake_case_				:  str       =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_       )

     snake_case_				:  List[str]       =			model(input_ids=lowercase_						,  pixel_values=lowercase_						,  attention_mask=lowercase_       )
     snake_case_				:  Dict       =			output[0]

     with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(lowercase_       )
         snake_case_				:  Union[str, Any]       =			FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_       )

         snake_case_				:  Union[str, Any]       =			model(input_ids=lowercase_						,  pixel_values=lowercase_						,  attention_mask=lowercase_       )
         snake_case_				:  Dict       =			after_output[0]
         snake_case_				:  Optional[int]       =			np.amax(np.abs(out_a - out_a       )       )
         self.assertLessEqual(lowercase_						,  1E-3       )

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: int						,  lowercase_	: int						,  lowercase_	: str						,  lowercase_	: List[Any]						,  lowercase_	: Optional[Any]=None						,  **lowercase_	: Optional[Any]       ):
     snake_case_,			snake_case_				:  Union[str, Any]       =			self.get_vision_text_model(lowercase_						,  lowercase_       )
     snake_case_				:  Any       =			{'''vision_model''': vision_model, '''text_model''': text_model}
     snake_case_				:  int       =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_       )

     snake_case_				:  Optional[Any]       =			model(
         input_ids=lowercase_						,  pixel_values=lowercase_						,  attention_mask=lowercase_						,  output_attentions=lowercase_       )

     snake_case_				:  List[str]       =			output.vision_model_output.attentions
     self.assertEqual(len(lowercase_       )						,  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_				:  int       =			to_atuple(vision_model.config.image_size       )
     snake_case_				:  Union[str, Any]       =			to_atuple(vision_model.config.patch_size       )
     snake_case_				:  int       =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     snake_case_				:  List[str]       =			num_patches + 1
     self.assertEqual(vision_attentions[0].shape[-3:]						,  (vision_config.num_attention_heads, seq_len, seq_len)       )

     snake_case_				:  int       =			output.text_model_output.attentions
     self.assertEqual(len(lowercase_       )						,  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 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[int]						,  lowercase_	: str						,  lowercase_	: str       ):
     pt_model.to(lowercase_       )
     pt_model.eval()

     # prepare inputs
     snake_case_				:  int       =			inputs_dict
     snake_case_				:  Union[str, Any]       =			{k: torch.tensor(v.tolist()       ) for k, v in flax_inputs.items()}

     with torch.no_grad():
         snake_case_				:  str       =			pt_model(**lowercase_       ).to_tuple()

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

     # PT -> Flax
     with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(lowercase_       )
         snake_case_				:  List[Any]       =			FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_						,  from_pt=lowercase_       )

     snake_case_				:  List[str]       =			fx_model_loaded(**lowercase_       ).to_tuple()
     self.assertEqual(len(lowercase_       )						,  len(lowercase_       )						,  '''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(lowercase_						,  pt_output.numpy()						,  4E-2       )

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

     pt_model_loaded.to(lowercase_       )
     pt_model_loaded.eval()

     with torch.no_grad():
         snake_case_				:  Union[str, Any]       =			pt_model_loaded(**lowercase_       ).to_tuple()

     self.assertEqual(len(lowercase_       )						,  len(lowercase_       )						,  '''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(lowercase_						,  pt_output_loaded.numpy()						,  4E-2       )

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: List[str]						,  lowercase_	: Tuple						,  lowercase_	: str       ):
     snake_case_				:  List[str]       =			VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_						,  lowercase_       )

     snake_case_				:  List[Any]       =			VisionTextDualEncoderModel(lowercase_       )
     snake_case_				:  str       =			FlaxVisionTextDualEncoderModel(lowercase_       )

     snake_case_				:  Dict       =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,  lowercase_       )
     snake_case_				:  Union[str, Any]       =			fx_state

     self.check_pt_flax_equivalence(lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: Union[str, Any]						,  lowercase_	: Dict						,  lowercase_	: int       ):
     snake_case_				:  List[Any]       =			VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_						,  lowercase_       )

     snake_case_				:  Dict       =			VisionTextDualEncoderModel(lowercase_       )
     snake_case_				:  Any       =			FlaxVisionTextDualEncoderModel(lowercase_       )

     snake_case_				:  Dict       =			load_flax_weights_in_pytorch_model(lowercase_						,  fx_model.params       )

     self.check_pt_flax_equivalence(lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Union[str, Any]       =			self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**lowercase_       )

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Tuple       =			self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**lowercase_       )

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Any       =			self.prepare_config_and_inputs()
     self.check_save_load(**lowercase_       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Optional[int]       =			self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**lowercase_       )

 @is_pt_flax_cross_test
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Any       =			self.prepare_config_and_inputs()
     snake_case_				:  int       =			config_inputs_dict.pop('''vision_config'''       )
     snake_case_				:  List[Any]       =			config_inputs_dict.pop('''text_config'''       )

     snake_case_				:  str       =			config_inputs_dict

     self.check_equivalence_pt_to_flax(lowercase_						,  lowercase_						,  lowercase_       )
     self.check_equivalence_flax_to_pt(lowercase_						,  lowercase_						,  lowercase_       )






 @slow
 def 		_snake_case      (  self	: str       ):
     snake_case_,			snake_case_				:  Dict       =			self.get_pretrained_model_and_inputs()

     snake_case_				:  Dict       =			model_a(**lowercase_       )
     snake_case_				:  Any       =			outputs[0]

     with tempfile.TemporaryDirectory() as tmp_dirname:
         model_a.save_pretrained(lowercase_       )
         snake_case_				:  Optional[Any]       =			FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_       )

         snake_case_				:  Dict       =			model_a(**lowercase_       )
         snake_case_				:  Optional[int]       =			after_outputs[0]
         snake_case_				:  Tuple       =			np.amax(np.abs(out_a - out_a       )       )
         self.assertLessEqual(lowercase_						,  1E-5       )





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

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  List[Any]       =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
         '''hf-internal-testing/tiny-random-vit'''						,  '''hf-internal-testing/tiny-bert'''						,  vision_from_pt=lowercase_						,  text_from_pt=lowercase_						,  )
     snake_case_				:  Optional[Any]       =			13
     snake_case_				:  Tuple       =			floats_tensor(
         [
             batch_size,
             model.config.vision_config.num_channels,
             model.config.vision_config.image_size,
             model.config.vision_config.image_size,
         ]       )
     snake_case_				:  List[str]       =			ids_tensor([batch_size, 4]						,  model.config.text_config.vocab_size       )
     snake_case_				:  str       =			random_attention_mask([batch_size, 4]       )
     snake_case_				:  str       =			{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

     return model, inputs

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Tuple						,  lowercase_	: Dict       ):
     snake_case_				:  Any       =			FlaxViTModel(lowercase_       )
     snake_case_				:  List[str]       =			FlaxBertModel(lowercase_       )
     return vision_model, text_model






 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  List[Any]       =			FlaxViTModelTester(self       )
     snake_case_				:  Tuple       =			FlaxBertModelTester(self       )
     snake_case_				:  int       =			vit_model_tester.prepare_config_and_inputs()
     snake_case_				:  Dict       =			bert_model_tester.prepare_config_and_inputs()

     snake_case_,			snake_case_				:  List[str]       =			vision_config_and_inputs

     snake_case_,			snake_case_,			snake_case_,			snake_case_				:  List[str]       =			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  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  List[Any]       =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
         '''hf-internal-testing/tiny-random-clip'''						,  '''hf-internal-testing/tiny-bert'''						,  vision_from_pt=lowercase_						,  text_from_pt=lowercase_						,  )
     snake_case_				:  Tuple       =			13
     snake_case_				:  Tuple       =			floats_tensor(
         [
             batch_size,
             model.config.vision_config.num_channels,
             model.config.vision_config.image_size,
             model.config.vision_config.image_size,
         ]       )
     snake_case_				:  int       =			ids_tensor([batch_size, 4]						,  model.config.text_config.vocab_size       )
     snake_case_				:  str       =			random_attention_mask([batch_size, 4]       )
     snake_case_				:  Optional[Any]       =			{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

     return model, inputs

 def 		_snake_case      (  self	: str						,  lowercase_	: Any						,  lowercase_	: List[Any]       ):
     snake_case_				:  str       =			FlaxCLIPVisionModel(lowercase_       )
     snake_case_				:  Optional[Any]       =			FlaxBertModel(lowercase_       )
     return vision_model, text_model






 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Union[str, Any]       =			FlaxCLIPVisionModelTester(self       )
     snake_case_				:  str       =			FlaxBertModelTester(self       )
     snake_case_				:  Tuple       =			clip_model_tester.prepare_config_and_inputs()
     snake_case_				:  str       =			bert_model_tester.prepare_config_and_inputs()

     snake_case_,			snake_case_				:  List[str]       =			vision_config_and_inputs

     snake_case_,			snake_case_,			snake_case_,			snake_case_				:  Optional[int]       =			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  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[str]       =			FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''						,  logit_scale_init_value=1.0       )
     snake_case_				:  Dict       =			VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''       )

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

     snake_case_				:  Union[str, Any]       =			model(**lowercase_       )

     # 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_				:  Dict       =			np.array([[1.2_28_47_27, 0.3_10_41_22]]       )

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










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





