






"""simple docstring"""







# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def 						lowercase__(A					,		A					,		A=0						)				->Tuple:




						"""simple docstring"""



						if name is None:
												lowercase__				:      Optional[Any]=       None
						else:
												lowercase__				:      List[str]=       "." * max(0					,		spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
												lowercase__				:      List[str]=       fmt.format(A						)

						# Print and recurse (if needed).
						if isinstance(A					,		A						):
												if msg is not None:
																		print(A						)
												for k in val.keys():
																		recursive_print(A					,		val[k]					,		spaces + 2						)
						elif isinstance(A					,		torch.Tensor						):
												print(A					,		":"					,		val.size()						)
						else:
												print(A					,		":"					,		A						)





def 						lowercase__(A					,		A					,		A					,		A					,		A						)				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												lowercase__				:      Any=       (num_heads, hidden_size, num_splits) + input_shape[1:]
												lowercase__				:      List[Any]=       param.view(*A						)
												lowercase__				:      List[str]=       param.transpose(0					,		2						)
												lowercase__				:      Optional[Any]=       param.transpose(1					,		2						).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												lowercase__				:      str=       (num_heads, num_splits, hidden_size) + input_shape[1:]
												lowercase__				:      Any=       param.view(*A						)
												lowercase__				:      List[str]=       param.transpose(0					,		1						).contiguous()
						lowercase__				:      Any=       param.view(*A						)
						return param





def 						lowercase__(A					,		A					,		A						)				->Tuple:




						"""simple docstring"""



						lowercase__				:      List[str]=       {}

						# old versions did not store training args
						lowercase__				:      str=       input_state_dict.get("args"					,		A						)
						if ds_args is not None:
												# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
												# from pprint import pprint
												# pprint(vars(ds_args))

												lowercase__				:      Optional[Any]=       ds_args.tokenizer_type
												lowercase__				:      Optional[int]=       ds_args.padded_vocab_size
												lowercase__				:      str=       ds_args.max_position_embeddings
												lowercase__				:      Union[str, Any]=       ds_args.hidden_size
												lowercase__				:      List[str]=       ds_args.num_layers
												lowercase__				:      Any=       ds_args.num_attention_heads
												lowercase__				:      Optional[int]=       ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
												# pprint(config)

						# The number of heads.
						lowercase__				:      List[str]=       config.num_attention_heads
						# The hidden_size per head.
						lowercase__				:      List[Any]=       config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												lowercase__				:      Any=       input_state_dict["checkpoint_version"]
						else:
												lowercase__				:      int=       0.0

						# The model.
						lowercase__				:      Optional[int]=       input_state_dict["model"]
						# The language model.
						lowercase__				:      Dict=       model["language_model"]
						# The embeddings.
						lowercase__				:      Optional[Any]=       lm["embedding"]

						# The word embeddings.
						lowercase__				:      Tuple=       embeddings["word_embeddings"]["weight"]
						# Truncate the embedding table to vocab_size rows.
						lowercase__				:      Any=       word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						lowercase__				:      Optional[int]=       word_embeddings

						# The position embeddings.
						lowercase__				:      Any=       embeddings["position_embeddings"]["weight"]
						assert pos_embeddings.size(0						) == config.max_position_embeddings and pos_embeddings.size(1						) == config.hidden_size
						# Store the position embeddings.
						lowercase__				:      int=       pos_embeddings

						# The token-type embeddings.
						lowercase__				:      Any=       embeddings["tokentype_embeddings"]["weight"]
						# Store the position embeddings.
						lowercase__				:      Tuple=       tokentype_embeddings

						# The transformer.
						lowercase__				:      List[Any]=       lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

						# The regex to extract layer names.
						lowercase__				:      Optional[Any]=       re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"						)

						# The simple map of names for "automated" rules.
						lowercase__				:      int=       {
						    "attention.dense": ".attention.output.dense.",
						    "self_attention.dense": ".attention.output.dense.",
						    "mlp.dense_h_to_4h": ".intermediate.dense.",
						    "mlp.dense_4h_to_h": ".output.dense.",
						}

						# Keep track of the attention/query/value tensor.
						lowercase__				:      Tuple=       None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												lowercase__				:      List[str]=       layer_re.match(A						)

												# Stop if that's not a layer
												if m is None:
																		break

												# The index of the layer.
												lowercase__				:      str=       int(m.group(1						)						)
												# The name of the operation.
												lowercase__				:      Optional[int]=       m.group(2						)
												# Is it a weight or a bias?
												lowercase__				:      int=       m.group(3						)

												# The name of the layer.
												lowercase__				:      Dict=       f'''bert.encoder.layer.{layer_idx}'''

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith("layernorm"						):
																		lowercase__				:      Tuple=       "attention.ln" if op_name.startswith("input"						) else "ln"
																		lowercase__				:      Dict=       val

												# Transpose the QKV matrix.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "weight":
																		# Make sure the QKV pointer is nil.
																		assert attention_qkv_weight is None, ""

																		lowercase__				:      List[Any]=       fix_query_key_value_ordering(A					,		A					,		3					,		A					,		A						)
																		# Store the tensor as we need the bias as well to interleave QKV and biases.
																		lowercase__				:      Optional[Any]=       out_val

												# Transpose the bias.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "bias":
																		# Make sure we read the weight tensor.
																		assert attention_qkv_weight is not None, ""

																		# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																		lowercase__				:      List[Any]=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		lowercase__				:      Tuple=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		lowercase__				:      Any=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		lowercase__				:      List[str]=       fix_query_key_value_ordering(A					,		A					,		3					,		A					,		A						)
																		# Split the bias.
																		lowercase__				:      Tuple=       out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		lowercase__				:      Tuple=       out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		lowercase__				:      Dict=       out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		lowercase__				:      Optional[Any]=       q
																		lowercase__				:      Optional[Any]=       q_bias
																		lowercase__				:      Optional[int]=       k
																		lowercase__				:      Tuple=       k_bias
																		lowercase__				:      Optional[Any]=       v
																		lowercase__				:      Optional[int]=       v_bias

																		# Clear the stored tensor.
																		lowercase__				:      Dict=       None

												# Copy weights and biases as is.
												elif weight_or_bias in ["weight", "bias"]:
																		lowercase__				:      Tuple=       megatron_to_transformers[op_name]
																		lowercase__				:      int=       val

    # The final layernorm.
						lowercase__				:      Tuple=       transformer["final_layernorm.weight"]
						lowercase__				:      Tuple=       transformer["final_layernorm.bias"]

						# The pooler.
						lowercase__				:      int=       lm["pooler"]

						# Store the matrix and the bias.
						lowercase__				:      Union[str, Any]=       pooler["dense.weight"]
						lowercase__				:      Optional[Any]=       pooler["dense.bias"]

						# The LM head from Megatron (for RACE).
						lowercase__				:      Dict=       model["lm_head"]

						# The transform matrix.
						lowercase__				:      List[str]=       lm_head["dense.weight"]
						lowercase__				:      Union[str, Any]=       lm_head["dense.bias"]

						# The transform LN.
						lowercase__				:      Dict=       lm_head["layernorm.weight"]
						lowercase__				:      Union[str, Any]=       lm_head["layernorm.bias"]

						# For the decoder, we replicate the weights.
						lowercase__				:      Union[str, Any]=       word_embeddings
						lowercase__				:      Any=       lm_head["bias"]

						# The classifier from Megatron (for MLNI).
						lowercase__				:      List[str]=       model["binary_head"]

						# Store the classifier.
						lowercase__				:      Optional[Any]=       binary_head["weight"]
						lowercase__				:      Optional[int]=       binary_head["bias"]

						# It should be done!
						return output_state_dict





def 						lowercase__()				->List[str]:




						"""simple docstring"""



						lowercase__				:      Any=       argparse.ArgumentParser()
						parser.add_argument("--print-checkpoint-structure"					,		action="store_true"						)
						parser.add_argument("path_to_checkpoint"					,		type=A					,		help="Path to the ZIP file containing the checkpoint"						)
						parser.add_argument(
						    "--config_file"					,		default=""					,		type=A					,		help="An optional config json file describing the pre-trained model."					,		)
						lowercase__				:      Optional[Any]=       parser.parse_args()

						# Extract the basename.
						lowercase__				:      Any=       os.path.dirname(args.path_to_checkpoint						)

						# Load the model.
						# the .zip is very optional, let's keep it for backward compatibility
						print(f'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''						)
						if args.path_to_checkpoint.endswith(".zip"						):
												with zipfile.ZipFile(args.path_to_checkpoint					,		"r"						) as checkpoint:
																		with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"						) as pytorch_dict:
																								lowercase__				:      Tuple=       torch.load(A					,		map_location="cpu"						)
						else:
												lowercase__				:      List[Any]=       torch.load(args.path_to_checkpoint					,		map_location="cpu"						)

						if args.config_file == "":
												# Default config of megatron-bert 345m
												lowercase__				:      Any=       MegatronBertConfig()

												# different megatron-bert-*-345m models have different vocab sizes, so override the default
												# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
												lowercase__				:      List[Any]=       input_state_dict["model"]["lm_head"]["bias"].numel()
						else:
												lowercase__				:      List[str]=       MegatronBertConfig.from_json_file(args.config_file						)

						# Convert.
						print("Converting"						)
						lowercase__				:      str=       convert_megatron_checkpoint(A					,		A					,		A						)

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(A					,		A						)

						# Store the config to file.
						print("Saving config"						)
						config.save_pretrained(A						)

						# Store the state_dict to file.
						lowercase__				:      List[str]=       os.path.join(A					,		"pytorch_model.bin"						)
						print(f'''Saving checkpoint to "{output_checkpoint_file}"'''						)
						torch.save(A					,		A						)


####################################################################################################

if __name__ == "__main__":
			main()

####################################################################################################














"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=7       ,  snake_case__=3       ,  snake_case__=18       ,  snake_case__=30       ,  snake_case__=400       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  snake_case__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  snake_case__=True       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       size if size is not None else {"shortest_edge": 20}
												lowercase__				:      int=       crop_size if crop_size is not None else {"height": 18, "width": 18}
												lowercase__				:      Union[str, Any]=       parent
												lowercase__				:      Optional[Any]=       batch_size
												lowercase__				:      List[str]=       num_channels
												lowercase__				:      str=       image_size
												lowercase__				:      Optional[int]=       min_resolution
												lowercase__				:      Union[str, Any]=       max_resolution
												lowercase__				:      List[Any]=       do_resize
												lowercase__				:      Any=       size
												lowercase__				:      Optional[Any]=       do_center_crop
												lowercase__				:      List[str]=       crop_size
												lowercase__				:      List[Any]=       do_normalize
												lowercase__				:      Optional[Any]=       image_mean
												lowercase__				:      Dict=       image_std
												lowercase__				:      Dict=       do_convert_rgb




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_convert_rgb": self.do_convert_rgb,
												}




						def 		UpperCAmelCase_       (   self       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__=False			):
												'''simple docstring'''
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								image_inputs.append(
																								    np.random.randint(
																								        255       ,  size=(self.num_channels, self.max_resolution, self.max_resolution)       ,  dtype=np.uinta			)			)
												else:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								lowercase__,	lowercase__				:      str=       np.random.choice(np.arange(self.min_resolution       ,  self.max_resolution			)       ,  2			)
																								image_inputs.append(np.random.randint(255       ,  size=(self.num_channels, width, height)       ,  dtype=np.uinta			)			)

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		lowercase__				:      Dict=       [Image.fromarray(np.moveaxis(snake_case__       ,  0       ,  -1			)			) for x in image_inputs]

												if torchify:
																		lowercase__				:      Tuple=       [torch.from_numpy(snake_case__			) for x in image_inputs]

												return image_inputs


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       CLIPImageProcessingTester(self			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class.from_dict(self.image_processor_dict			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 20}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}			)

												lowercase__				:      Dict=       self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42       ,  crop_size=84			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 42}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 84, "width": 84}			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      Optional[Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      List[str]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Dict=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Any=       self.image_processing_class(**self.image_processor_dict			)
												# create random numpy tensors
												lowercase__				:      Tuple=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  numpify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  np.ndarray			)

												# Test not batched input
												lowercase__				:      Dict=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      int=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Optional[Any]=       self.image_processing_class(**self.image_processor_dict			)
												# create random PyTorch tensors
												lowercase__				:      Union[str, Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  torchify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  torch.Tensor			)

												# Test not batched input
												lowercase__				:      Any=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Union[str, Any]=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       CLIPImageProcessingTester(self       ,  num_channels=4			)
												lowercase__				:      Optional[Any]=       3




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      int=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      Optional[int]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Any=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )






