




"""simple docstring"""





import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A				=							get_logger(__name__)


A				=							r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""



class   a__					:


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Dict   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray):


						"""simple docstring"""




						raise NotImplementedError(
						    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






class   a__					:


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray):


						"""simple docstring"""




						raise NotImplementedError(
						    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






class   a__					(						__magic_name__ ):


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Any   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int   ,	**UpperCamelCase_   :     int):


						"""simple docstring"""




						for processor in self:
									__UpperCAmelCase						:			Any												=       inspect.signature(processor.__call__).parameters
									if len(UpperCamelCase_) > 3:
												if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
															raise ValueError(
															    F"Make sure that all the required parameters: {list(function_args.keys())} for "
															    F"{processor.__class__} are passed to the logits processor.")
												__UpperCAmelCase						:			Union[str, Any]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	**UpperCamelCase_)
									else:
												__UpperCAmelCase						:			Optional[int]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     float):


						"""simple docstring"""




						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or not (temperature > 0):
									raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}")

						__UpperCAmelCase						:			Optional[Any]												=       temperature


			def __call__(   self   :     Optional[int]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       scores / self.temperature
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     float   ,	UpperCamelCase_   :     float = -float("Inf")   ,	UpperCamelCase_   :     int = 1):


						"""simple docstring"""




						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or (top_p < 0 or top_p > 1.0):
									raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}")
						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or (min_tokens_to_keep < 1):
									raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}")

						__UpperCAmelCase						:			Optional[int]												=       top_p
						__UpperCAmelCase						:			List[Any]												=       filter_value
						__UpperCAmelCase						:			Tuple												=       min_tokens_to_keep


			def __call__(   self   :     str   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       lax.top_k(UpperCamelCase_   ,	scores.shape[-1])

						__UpperCAmelCase						:			int												=       jnp.full_like(UpperCamelCase_   ,	self.filter_value)
						__UpperCAmelCase						:			List[Any]												=       jax.nn.softmax(UpperCamelCase_   ,	axis=-1).cumsum(axis=-1)
						__UpperCAmelCase						:			Tuple												=       cumulative_probs < self.top_p

						# include the token that is higher than top_p as well
						__UpperCAmelCase						:			Tuple												=       jnp.roll(UpperCamelCase_   ,	1)
						score_mask |= score_mask.at[:, 0].set(UpperCamelCase_)

						# min tokens to keep
						__UpperCAmelCase						:			List[str]												=       score_mask.at[:, : self.min_tokens_to_keep].set(UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, Any]												=       jnp.where(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       jax.lax.sort_key_val(UpperCamelCase_   ,	UpperCamelCase_)[-1]

						return next_scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     float = -float("Inf")   ,	UpperCamelCase_   :     int = 1):


						"""simple docstring"""




						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or top_k <= 0:
									raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}")

						__UpperCAmelCase						:			List[str]												=       max(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       filter_value


			def __call__(   self   :     Optional[int]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       scores.shape
						__UpperCAmelCase						:			Union[str, Any]												=       jnp.full(batch_size * vocab_size   ,	self.filter_value)

						__UpperCAmelCase						:			Union[str, Any]												=       min(self.top_k   ,	scores.shape[-1])  # Safety check
						__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       lax.top_k(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       jnp.broadcast_to((jnp.arange(UpperCamelCase_) * vocab_size)[:, None]   ,	(batch_size, topk)).flatten()
						__UpperCAmelCase						:			str												=       topk_scores.flatten()
						__UpperCAmelCase						:			int												=       topk_indices.flatten() + shift

						__UpperCAmelCase						:			str												=       next_scores_flat.at[topk_indices_flat].set(UpperCamelCase_)
						__UpperCAmelCase						:			Any												=       next_scores_flat.reshape(UpperCamelCase_   ,	UpperCamelCase_)
						return next_scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Union[str, Any]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       bos_token_id


			def __call__(   self   :     int   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       jnp.full(scores.shape   ,	-float("inf"))

						__UpperCAmelCase						:			Optional[int]												=       1 - jnp.bool_(cur_len - 1)

						__UpperCAmelCase						:			List[Any]												=       jnp.where(UpperCamelCase_   ,	new_scores.at[:, self.bos_token_id].set(0)   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       max_length
						__UpperCAmelCase						:			Optional[Any]												=       eos_token_id


			def __call__(   self   :     int   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       jnp.full(scores.shape   ,	-float("inf"))

						__UpperCAmelCase						:			Dict												=       1 - jnp.bool_(cur_len - self.max_length + 1)

						__UpperCAmelCase						:			Any												=       jnp.where(UpperCamelCase_   ,	new_scores.at[:, self.eos_token_id].set(0)   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or min_length < 0:
									raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}")

						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) or eos_token_id < 0:
									raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}")

						__UpperCAmelCase						:			List[Any]												=       min_length
						__UpperCAmelCase						:			Tuple												=       eos_token_id


			def __call__(   self   :     Dict   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       1 - jnp.clip(cur_len - self.min_length   ,	0   ,	1)

						__UpperCAmelCase						:			Optional[int]												=       jnp.where(UpperCamelCase_   ,	scores.at[:, self.eos_token_id].set(-float("inf"))   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     int   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       list(UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       begin_index


			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       1 - jnp.bool_(cur_len - self.begin_index)

						__UpperCAmelCase						:			str												=       jnp.where(UpperCamelCase_   ,	scores.at[:, self.begin_suppress_tokens].set(-float("inf"))   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Dict   ,	UpperCamelCase_   :     list):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       list(UpperCamelCase_)


			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       scores.at[..., self.suppress_tokens].set(-float("inf"))

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       dict(UpperCamelCase_)
						# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
						# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
						# Indexes without forced tokens will have a negative value.
						__UpperCAmelCase						:			int												=       jnp.ones((max(force_token_map.keys()) + 1)   ,	dtype=jnp.intaa) * -1
						for index, token in force_token_map.items():
									if token is not None:
												__UpperCAmelCase						:			Dict												=       force_token_array.at[index].set(UpperCamelCase_)
						__UpperCAmelCase						:			str												=       jnp.intaa(UpperCamelCase_)


			def __call__(   self   :     Any   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						def _force_token(UpperCamelCase_   :     List[Any]):
									__UpperCAmelCase						:			Optional[Any]												=       scores.shape[0]
									__UpperCAmelCase						:			int												=       self.force_token_array[generation_idx]

									__UpperCAmelCase						:			List[Any]												=       jnp.ones_like(UpperCamelCase_   ,	dtype=scores.dtype) * -float("inf")
									__UpperCAmelCase						:			List[str]												=       jnp.zeros((batch_size, 1)   ,	dtype=scores.dtype)
									__UpperCAmelCase						:			Dict												=       lax.dynamic_update_slice(UpperCamelCase_   ,	UpperCamelCase_   ,	(0, current_token))
									return new_scores

						__UpperCAmelCase						:			List[str]												=       lax.cond(
						    cur_len >= self.force_token_array.shape[0]   ,	lambda: scores   ,	lambda: lax.cond(
						        self.force_token_array[cur_len] >= 0   ,	lambda: _force_token(UpperCamelCase_)   ,	lambda: scores   ,	)   ,	)
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       generate_config.eos_token_id
						__UpperCAmelCase						:			List[str]												=       generate_config.no_timestamps_token_id
						__UpperCAmelCase						:			Dict												=       generate_config.no_timestamps_token_id + 1

						__UpperCAmelCase						:			Tuple												=       decoder_input_length + 1

						if generate_config.is_multilingual:
									# room for language token and task token
									self.begin_index += 2
						if hasattr(UpperCamelCase_   ,	"max_initial_timestamp_index"):
									__UpperCAmelCase						:			Optional[int]												=       generate_config.max_initial_timestamp_index
						else:
									__UpperCAmelCase						:			str												=       model_config.vocab_size
						if self.max_initial_timestamp_index is None:
									__UpperCAmelCase						:			Union[str, Any]												=       model_config.vocab_size


			def __call__(   self   :     Tuple   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       scores.at[:, self.no_timestamps_token_id].set(-float("inf"))

						def handle_pairs(UpperCamelCase_   :     str   ,	UpperCamelCase_   :     List[Any]):
									__UpperCAmelCase						:			Dict												=       jnp.where((cur_len - self.begin_index) >= 1   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			Dict												=       jnp.where(
									    input_ids_k[cur_len - 1] >= self.timestamp_begin   ,	True and last_was_timestamp   ,	UpperCamelCase_   ,	)

									__UpperCAmelCase						:			str												=       jnp.where((cur_len - self.begin_index) < 2   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			List[Any]												=       jnp.where(
									    input_ids_k[cur_len - 2] >= self.timestamp_begin   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	)

									return jnp.where(
									    UpperCamelCase_   ,	jnp.where(
									        penultimate_was_timestamp > 0   ,	scores_k.at[self.timestamp_begin :].set(-float("inf"))   ,	scores_k.at[: self.eos_token_id].set(-float("inf"))   ,	)   ,	UpperCamelCase_   ,	)

						__UpperCAmelCase						:			List[Any]												=       jax.vmap(UpperCamelCase_)(UpperCamelCase_   ,	UpperCamelCase_)

						__UpperCAmelCase						:			Dict												=       jnp.where(cur_len == self.begin_index   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       jnp.where(
						    self.max_initial_timestamp_index is not None   ,	True and apply_max_initial_timestamp   ,	UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Optional[Any]												=       self.timestamp_begin + self.max_initial_timestamp_index

						__UpperCAmelCase						:			str												=       jnp.where(
						    UpperCamelCase_   ,	scores.at[:, last_allowed + 1 :].set(-float("inf"))   ,	UpperCamelCase_   ,	)

						# if sum of probability over timestamps is above any other token, sample timestamp
						__UpperCAmelCase						:			Dict												=       jax.nn.log_softmax(UpperCamelCase_   ,	axis=-1)

						def handle_cumulative_probs(UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[str]):
									__UpperCAmelCase						:			int												=       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,	axis=-1)
									__UpperCAmelCase						:			int												=       jnp.max(logprobs_k[: self.timestamp_begin])
									return jnp.where(
									    timestamp_logprob > max_text_token_logprob   ,	scores_k.at[: self.timestamp_begin].set(-float("inf"))   ,	UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Optional[Any]												=       jax.vmap(UpperCamelCase_)(UpperCamelCase_   ,	UpperCamelCase_)

						return scores







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 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Dict:
    if isinstance(UpperCamelCase_			,							collections.abc.Iterable						):
        return x
    return (x, x)



@require_tf
class      _UpperCAmelCase      :




     def 		snake_case_		(					self			,    a__			,    a__):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__					=       VisionTextDualEncoderConfig.from_vision_text_configs(a__			,    a__)

         A__					=       TFVisionTextDualEncoderModel(a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         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			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         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			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       {'''vision_model''': vision_model, '''text_model''': text_model}
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(**a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         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			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)
         A__					=       output[0].numpy()

         with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(a__)
             A__					=       TFVisionTextDualEncoderModel.from_pretrained(a__)

             A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)
             A__					=       after_output[0].numpy()
             A__					=       np.amax(np.abs(out_a - out_a))
             self.assertLessEqual(a__			,    1e-5)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(
             input_ids=a__			,    pixel_values=a__			,    attention_mask=a__			,    output_attentions=a__)

         A__					=       output.vision_model_output.attentions
         self.assertEqual(len(a__)			,    vision_config.num_hidden_layers)

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

         A__					=       output.text_model_output.attentions
         self.assertEqual(len(a__)			,    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			,    a__			,    a__			,    a__):
         A__					=       np.abs((a - b)).max()
         self.assertLessEqual(a__			,    a__			,    F"Difference between torch and flax is {diff} (>= {tol}).")




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_model(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_save_load(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**a__)




     @slow
     def 		snake_case_		(					self):
         A__			,						A__					=       self.get_pretrained_model_and_inputs()

         A__					=       model_a(**a__)
         A__					=       outputs[0].numpy()

         with tempfile.TemporaryDirectory() as tmp_dirname:
             model_a.save_pretrained(a__)
             A__					=       TFVisionTextDualEncoderModel.from_pretrained(a__)

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







@require_tf
class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''hf-internal-testing/tiny-random-vit'''			,    '''hf-internal-testing/tiny-random-bert''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       TFViTModel(a__			,    name='''vision_model''')
         A__					=       TFBertModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFViTModelTester(self)
         A__					=       TFBertModelTester(self)
         A__					=       vit_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       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      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
         # just reinitialize it.
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''Rocketknight1/tiny-random-deit-tf'''			,    '''hf-internal-testing/tiny-random-roberta''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(
             input_ids=a__			,    pixel_values=a__			,    attention_mask=a__			,    output_attentions=a__)

         A__					=       output.vision_model_output.attentions
         self.assertEqual(len(a__)			,    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)
         A__					=       to_atuple(vision_model.config.image_size)
         A__					=       to_atuple(vision_model.config.patch_size)
         A__					=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         A__					=       num_patches + 2
         self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len))

         A__					=       output.text_model_output.attentions
         self.assertEqual(len(a__)			,    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			,    a__			,    a__):
         A__					=       TFDeiTModel(a__			,    name='''vision_model''')
         A__					=       TFRobertaModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFDeiTModelTester(self)
         A__					=       TFRobertaModelTester(self)
         A__					=       vit_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       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      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''Rocketknight1/tiny-random-clip-tf'''			,    '''hf-internal-testing/tiny-random-bert''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       TFCLIPVisionModel(a__			,    name='''vision_model''')
         A__					=       TFBertModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFCLIPVisionModelTester(self)
         A__					=       TFBertModelTester(self)
         A__					=       clip_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       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      _UpperCAmelCase      (							unittest.TestCase  ):




     @slow
     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_pretrained(
             '''clip-italian/clip-italian'''			,    logit_scale_init_value=1.0			,    from_pt=a__)
         A__					=       VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

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

         A__					=       model(**a__)

         # 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])			,    )

         A__					=       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.numpy()			,    a__			,    atol=1e-3))
