

from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_						     =    logging.get_logger(__name__)  # pylint: disable=invalid-name






class _UpperCamelCase							(							SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: Union[str, Any]      ,      a							: Optional[int]      ,      a							: Optional[Any] = None      ,      a							: Tuple = None	)							->       List[str]:

				"""simple docstring"""

				super().__init__()

				SCREAMING_SNAKE_CASE   :      int				     =       learnable

				if self.learnable:
					assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
					assert length is not None, "learnable=True requires `length` to be set"

					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.zeros(_lowercase      ,      _lowercase	)
				else:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       None

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.nn.Parameter(_lowercase	)






class _UpperCamelCase							(							SCREAMING_SNAKE_CASE__   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			def __init__(			self							: int      ,      a							: List[str]      ,      a							: int      ,      a							: List[Any]      ,      a							: int      ,      a							: Union[str, Any]      ,      a							: List[Any]      ,      )							->       Optional[Any]:

				"""simple docstring"""

				super().__init__()

				self.register_modules(
				    vqvae=_lowercase      ,      transformer=_lowercase      ,      text_encoder=_lowercase      ,      tokenizer=_lowercase      ,      scheduler=_lowercase      ,      learned_classifier_free_sampling_embeddings=_lowercase      ,      )
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: Optional[int]      ,      a							: Any	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(_lowercase	) if isinstance(_lowercase      ,      _lowercase	) else 1

				# get prompt text embeddings
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.tokenizer(
				    _lowercase      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      return_tensors="pt"      ,      )
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					SCREAMING_SNAKE_CASE   :      Any				     =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
					logger.warning(
					    "The following part of your input was truncated because CLIP can only handle sequences up to"
					    F" {self.tokenizer.model_max_length} tokens: {removed_text}"	)
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       text_input_ids[:, : self.tokenizer.model_max_length]
				SCREAMING_SNAKE_CASE   :      Any				     =       self.text_encoder(text_input_ids.to(self.device	)	)[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       prompt_embeds / prompt_embeds.norm(dim=-1      ,      keepdim=_lowercase	)

				# duplicate text embeddings for each generation per prompt
				SCREAMING_SNAKE_CASE   :      Tuple				     =       prompt_embeds.repeat_interleave(_lowercase      ,      dim=0	)

				if do_classifier_free_guidance:
					if self.learned_classifier_free_sampling_embeddings.learnable:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.learned_classifier_free_sampling_embeddings.embeddings
						SCREAMING_SNAKE_CASE   :      Any				     =       negative_prompt_embeds.unsqueeze(0	).repeat(_lowercase      ,      1      ,      1	)
					else:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [""""""] * batch_size

						SCREAMING_SNAKE_CASE   :      Tuple				     =       text_input_ids.shape[-1]
						SCREAMING_SNAKE_CASE   :      Dict				     =       self.tokenizer(
						    _lowercase      ,      padding="max_length"      ,      max_length=_lowercase      ,      truncation=_lowercase      ,      return_tensors="pt"      ,      )
						SCREAMING_SNAKE_CASE   :      Dict				     =       self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
						# See comment for normalizing text embeddings
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1      ,      keepdim=_lowercase	)

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       negative_prompt_embeds.shape[1]
					SCREAMING_SNAKE_CASE   :      int				     =       negative_prompt_embeds.repeat(1      ,      _lowercase      ,      1	)
					SCREAMING_SNAKE_CASE   :      int				     =       negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,      _lowercase      ,      -1	)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.cat([negative_prompt_embeds, prompt_embeds]	)

				return prompt_embeds
			@torch.no_grad()
			def __call__(			self							: int      ,      a							: Optional[Any]      ,      a							: Union[str, Any] = 100      ,      a							: Optional[int] = 5.0      ,      a							: Optional[int] = 1.0      ,      a							: List[Any] = 1      ,      a							: int = None      ,      a							: Optional[Any] = None      ,      a							: Any = "pil"      ,      a							: str = True      ,      a							: str = None      ,      a							: Optional[Any] = 1      ,      )							->       Union[ImagePipelineOutput, Tuple]:

				"""simple docstring"""

				if isinstance(_lowercase      ,      _lowercase	):
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1
				elif isinstance(_lowercase      ,      _lowercase	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       len(_lowercase	)
				else:
					raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowercase	)}"	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       batch_size * num_images_per_prompt

				SCREAMING_SNAKE_CASE   :      List[str]				     =       guidance_scale > 1.0

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self._encode_prompt(_lowercase      ,      _lowercase      ,      _lowercase	)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(_lowercase      ,      _lowercase	) or callback_steps <= 0)
				):
					raise ValueError(
					    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
					    F" {type(_lowercase	)}."	)

				# get the initial completely masked latents unless the user supplied it

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.transformer.num_vector_embeds - 1
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.full(_lowercase      ,      _lowercase	).to(self.device	)
				else:
					if latents.shape != latents_shape:
						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"	)
					if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
						raise ValueError(
						    "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
						    F" {self.transformer.num_vector_embeds - 1} (inclusive)."	)
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       latents.to(self.device	)

				# set timesteps
				self.scheduler.set_timesteps(_lowercase      ,      device=self.device	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.scheduler.timesteps.to(self.device	)

				SCREAMING_SNAKE_CASE   :      int				     =       latents

				for i, t in enumerate(self.progress_bar(_lowercase	)	):
					# expand the sample if we are doing classifier free guidance
					SCREAMING_SNAKE_CASE   :      Dict				     =       torch.cat([sample] * 2	) if do_classifier_free_guidance else sample

					# predict the un-noised image
					# model_output == `log_p_x_0`
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.transformer(_lowercase      ,      encoder_hidden_states=_lowercase      ,      timestep=_lowercase	).sample

					if do_classifier_free_guidance:
						SCREAMING_SNAKE_CASE   :      Any				     =       model_output.chunk(2	)
						SCREAMING_SNAKE_CASE   :      Any				     =       model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
						model_output -= torch.logsumexp(_lowercase      ,      dim=1      ,      keepdim=_lowercase	)

					SCREAMING_SNAKE_CASE   :      str				     =       self.truncate(_lowercase      ,      _lowercase	)

					# remove `log(0)`'s (`-inf`s)
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model_output.clamp(-70	)

					# compute the previous noisy sample x_t -> x_t-1
					SCREAMING_SNAKE_CASE   :      List[str]				     =       self.scheduler.step(_lowercase      ,      timestep=_lowercase      ,      sample=_lowercase      ,      generator=_lowercase	).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(_lowercase      ,      _lowercase      ,      _lowercase	)

				SCREAMING_SNAKE_CASE   :      str				     =       self.vqvae.config.vq_embed_dim
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.vqvae.quantize.get_codebook_entry(_lowercase      ,      shape=_lowercase	)
				SCREAMING_SNAKE_CASE   :      int				     =       self.vqvae.decode(_lowercase      ,      force_not_quantize=_lowercase	).sample

				SCREAMING_SNAKE_CASE   :      int				     =       (image / 2 + 0.5).clamp(0      ,      1	)
				SCREAMING_SNAKE_CASE   :      int				     =       image.cpu().permute(0      ,      2      ,      3      ,      1	).numpy()

				if output_type == "pil":
					SCREAMING_SNAKE_CASE   :      Any				     =       self.numpy_to_pil(_lowercase	)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_lowercase	)


			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Optional[int]      ,      a							: List[Any]	)							->       torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.sort(_lowercase      ,      1      ,      descending=_lowercase	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.exp(_lowercase	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       sorted_p_x_0.cumsum(dim=1	) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.full_like(keep_mask[:, 0:1, :]      ,      _lowercase	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.cat((all_true, keep_mask)      ,      dim=1	)
				SCREAMING_SNAKE_CASE   :      int				     =       keep_mask[:, :-1, :]

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       keep_mask.gather(1      ,      indices.argsort(1	)	)

				SCREAMING_SNAKE_CASE   :      int				     =       log_p_x_0.clone()

				SCREAMING_SNAKE_CASE   :      Any				     =       -torch.inf  # -inf = log(0)

				return rv


import inspect
import unittest
from typing import List

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFEfficientFormerForImageClassification,
			    TFEfficientFormerForImageClassificationWithTeacher,
			    TFEfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_tf_efficientformer import (
			    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: int      ,      a							: Optional[Any]      ,      a							: int = 13      ,      a							: int = 64      ,      a							: int = 2      ,      a							: int = 3      ,      a							: int = 3      ,      a							: bool = True      ,      a							: bool = True      ,      a							: int = 128      ,      a							: Any=[16, 32, 64, 128]      ,      a							: int = 7      ,      a							: int = 4      ,      a							: int = 37      ,      a							: str = "gelu"      ,      a							: float = 0.1      ,      a							: float = 0.1      ,      a							: int = 10      ,      a							: float = 0.02      ,      a							: int = 2      ,      a							: int = 1      ,      a							: int = 128      ,      a							: List[int] = [2, 2, 2, 2]      ,      a							: int = 2      ,      a							: int = 2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       image_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      int				     =       is_training
				SCREAMING_SNAKE_CASE   :      str				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      str				     =       encoder_stride
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_attention_outputs
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       embed_dim + 1
				SCREAMING_SNAKE_CASE   :      Dict				     =       resolution
				SCREAMING_SNAKE_CASE   :      str				     =       depths
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_sizes
				SCREAMING_SNAKE_CASE   :      str				     =       dim
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mlp_expansion_ratio
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      int				     =       None
				if self.use_labels:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ids_tensor([self.batch_size]      ,      self.type_sequence_label_size	)

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_config()

				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

				"""simple docstring"""

				return EfficientFormerConfig(
				    image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      is_decoder=a      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      resolution=self.resolution      ,      depths=self.depths      ,      hidden_sizes=self.hidden_sizes      ,      dim=self.dim      ,      mlp_expansion_ratio=self.mlp_expansion_ratio      ,      )
			def 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, Any]      ,      a							: Tuple      ,      a							: Union[str, Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerModel(config=a	)
				SCREAMING_SNAKE_CASE   :      int				     =       model(a      ,      training=a	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: str      ,      a							: Union[str, Any]      ,      a							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(a      ,      labels=a      ,      training=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				SCREAMING_SNAKE_CASE   :      Dict				     =       1
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       TFEfficientFormerForImageClassification(a	)

				SCREAMING_SNAKE_CASE   :      str				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model(a      ,      labels=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)	)






			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__           =(
			    {
			        'feature-extraction': TFEfficientFormerModel,
			        'image-classification': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__           =False

			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       TFEfficientFormerModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ConfigTester(
				    self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			@unittest.skip(reason="EfficientFormer does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				pass
			@unittest.skip(reason="EfficientFormer does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: Any	)							->       Dict:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       inspect.signature(model.call	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Dict				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				def check_hidden_states_output(a							: Optional[int]      ,      a							: Any      ,      a							: List[Any]	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      str				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       getattr(
					    self.model_tester      ,      "expected_num_hidden_layers"      ,      self.model_tester.num_hidden_layers + 1	)
					self.assertEqual(len(a	)      ,      a	)

					if hasattr(self.model_tester      ,      "encoder_seq_length"	):
						SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.encoder_seq_length
						if hasattr(self.model_tester      ,      "chunk_length"	) and self.model_tester.chunk_length > 1:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       seq_length * self.model_tester.chunk_length
					else:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.seq_length

					self.assertListEqual(
					    list(hidden_states[-1].shape[-2:]	)      ,      [seq_length, self.model_tester.hidden_size]      ,      )

					if config.is_encoder_decoder:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       outputs.decoder_hidden_states

						self.asseretIsInstance(a      ,      (list, tuple)	)
						self.assertEqual(len(a	)      ,      a	)
						SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       getattr(self.model_tester      ,      "decoder_seq_length"      ,      a	)

						self.assertListEqual(
						    list(hidden_states[-1].shape[-2:]	)      ,      [decoder_seq_length, self.model_tester.hidden_size]      ,      )

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      int				     =       True
					check_hidden_states_output(a      ,      a      ,      a	)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE   :      str				     =       True

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]      ,      a							: List[str]      ,      a							: Optional[Any]=False	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       super()._prepare_for_class(a      ,      a      ,      return_labels=a	)

				if return_labels:
					if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
						del inputs_dict["labels"]

				return inputs_dict
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"	)
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)
			@slow
			def 							__UpperCamelCase (			self							: int	)							->       Any:

				"""simple docstring"""

				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFEfficientFormerModel.from_pretrained(a	)
					self.assertIsNotNone(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True

				SCREAMING_SNAKE_CASE   :      str				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       getattr(self.model_tester      ,      "encoder_seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       getattr(self.model_tester      ,      "key_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       getattr(self.model_tester      ,      "chunk_length"      ,      a	)

				if chunk_length is not None and hasattr(self.model_tester      ,      "num_hashes"	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Any				     =       True
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       False
					SCREAMING_SNAKE_CASE   :      Tuple				     =       True
					SCREAMING_SNAKE_CASE   :      Any				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Tuple				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(a	)      ,      self.model_tester.num_attention_outputs	)

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       True
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(a	)      ,      self.model_tester.num_attention_outputs	)

					if chunk_length is not None:
						self.assertListEqual(
						    list(attentions[0].shape[-4:]	)      ,      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,      )
					else:
						self.assertListEqual(
						    list(attentions[0].shape[-3:]	)      ,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,      )






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					# Prepare our model
					SCREAMING_SNAKE_CASE   :      str				     =       model_class(a	)
					# These are maximally general inputs for the model, with multiple None dimensions
					# Hopefully this will catch any conditionals that fail for flexible shapes
					SCREAMING_SNAKE_CASE   :      Any				     =       {
					    key: tf.keras.Input(shape=val.shape[1:]      ,      dtype=val.dtype      ,      name=a	)
					    for key, val in model.input_signature.items()
					    if key in model.dummy_inputs
					}
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(a	)
					self.assertTrue(outputs_dict is not None	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				return (
				    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"	)
				    if is_vision_available()
				    else None
				)
			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Any				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_processor(images=a      ,      return_tensors="tf"	)
				# forward pass
				SCREAMING_SNAKE_CASE   :      Tuple				     =       model(**a      ,      training=a	)
				# verify the logits
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       tf.constant([-0.0555, 0.4825, -0.0852]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,      a      ,      atol=1e-4	)	)






			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    "snap-research/efficientformer-l1-300"	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Dict				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      str				     =       image_processor(images=a      ,      return_tensors="tf"	)
				# forward pass
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**a      ,      training=a	)
				# verify the logits
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.constant([-0.1312, 0.4353, -1.0499]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,      a      ,      atol=1e-4	)	)